Average customer rating:
- Excellent book for pattern analysis and classification!
- The book should change its title
- Ok, but too much math destroys the intuition...
- The best Pattern Recognition textbook I know
- Great Insights, but a hard read
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
Manufacturer: Springer
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ASIN: 0387310738 |
Book Description
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download
Customer Reviews:
Excellent book for pattern analysis and classification!.......2007-10-01
Excellent book for pattern analysis and classification! It begins with basic data curve fitting, linear classification models and ends with combining models (tree-based models, graphical models, etc.). Contains great number of examples and exercises. Very good introductory for beginners in pattern analysis, excellent companion for academics and researchers.
The book should change its title.......2007-09-25
This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.
For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)
These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).
Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.
Ok, but too much math destroys the intuition..........2007-09-09
This book is a fairly thorough overview of typical topics employed in a graduate machine learning course. However, from page 5 on, expect to see more equations on each page than paragraphs of text (with most of the remaining text explaining the context of the variables within the equations). Now, for someone such as myself who enjoys mathematics, this is not a problem. However, I would not recommend this book for someone with a mathematics background that is in any way weak. Furthermore, there is a more fundamental problem with the presentation of the material that warrants this book no more than a 3-star rating: the simple intuitiveness of the concepts is completely lost within the mathematics. Instead of explaining what variables represent and leaving it to the reader to figure out what is going on, this book could be made much more approachable by simply stating the intuition behind the equations. Take the sum rule, one of the first theorems in the book, for an example of how the author muddles what is effectively a basic and intuitive concept: the book has a fairly lengthy definition of several variables representing concepts such as "the number of observations in which x_ij appears" prior to presenting a summation over all y-variables (a notational convention that the author admits is "cumbersome" on the next page, and states that "there will be no need for such pedantry" as that which he proceeds to perpetrate throughout the book!), while he could have simply presented the simplified sum on the following page (p(X) = sum(p(X,Y), Y)) and it would be immediately clear to most readers what he was attempting to explain. He could also simply state the intuition behind the theorem in English, that summing over every event yields a probability of one, and therefore summing over all events in which a variable appears effectively marginalizes the variable (something he comes close to doing after the presentation of the equation, but by then, the reader's time has already been wasted). Similar examples abound throughout the book, becoming particularly bad during the middle sections, when the techniques begin to become less intuitive.
As another reader mentioned, the author also commits the serious mistake of using pi for a symbol other than the constant or the product operator, which muddles the equations on a skim and forces the reader to refer back to the variable definitions to determine the context.
Having done work in machine learning's applied cousin, data mining, and thus having used many of the techniques presented in the book in actual research, I can't help but think that the presentation of the book's content could be much clearer. When doing work in the field, we can look up the equations as-needed; it is the knowledge of *when* and *how* to apply or extend these techniques that is more important, and that is the area in which I feel this book is lacking.
The best Pattern Recognition textbook I know.......2007-07-17
This book brings the most updated research in this field. The writing stile combines common-sense intuitive explanations with precise mathematical formulations. A lot of colorful figures support the text and help the reader to understand and absorb the described ideas. Short biographies of scientists like Bayes, Laplace, Gauss etc. (which unfortunately substantially drop after the Ch. 2) provide a brief glancing on humans which are behind these great names. The author makes connections between the different chapters, which help the reader to see a wide picture. But don't expect for an easy work. As every deep scientific text it is sometimes fluent and fun, and sometimes demands an effort, rereading the same text again and again, and referring to other references. Personally I feel a great satisfaction when after such an effort the concept became clear to me.
The other useful feature is solved exercises which are available for download from the authors' web site [..]
The main drawback of this book is a relative small amount of detailed examples. As an experienced educator, I know that "a single good example could worth a thousand explanations". It probably will be not an issue with appearance of the practical companion volume (Bishop and Nabney, 2008). The reference to the future (2008) still un-existed publication is unusual, fresh-thinking, and right idea.
With this book C. Bishop continues his "tradition" of writing deep and important scientific books which was started with the "Neural Networks for Pattern Recognition".
A short comment to the reviewer "lew lwndn123", who is deeply disappointed by the fact that this is a textbook. Yes, it is a textbook, and it is clearly written in the "Book Description". It is unfair to "kill" the book just because you didn't really check what you are going to buy, especially you admit that "as a textbook, this is very good text, and deserves 5 starts". I think it will be a decent step if you will correct your review.
Great Insights, but a hard read.......2007-06-16
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.
But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
Amazon.com
"Intended as an upper-level undergraduate or introductory graduate text in computer science theory," this book lucidly covers the key concepts and theorems of the theory of computation. The presentation is remarkably clear; for example, the "proof idea," which offers the reader an intuitive feel for how the proof was constructed, accompanies many of the theorems and a proof. Introduction to the Theory of Computation covers the usual topics for this type of text plus it features a solid section on complexity theory--including an entire chapter on space complexity. The final chapter introduces more advanced topics, such as the discussion of complexity classes associated with probabilistic algorithms.
Book Description
Michael Sipser's emphasis on unifying computer science theory - rather than offering a collection of low-level details - sets the book apart, as do his intuitive explanations. Throughout the book, Sipser builds students' knowledge of conceptual tools used in computer science, the aesthetic sense they need to create elegant systems, and the ability to think through problems on their own.
Customer Reviews:
My choice for textbook in my computation theory class.......2007-10-01
I recently encountered this book at a publisher's booth at a computer conference and read it on the ride back home. This morning I made a trip to the college bookstore and notified them that it is the textbook that I will be using in my computation theory class this spring.
The chapter titles are:
0) Introduction - this chapter contains the fundamental mathematical background of sets, functions, graphs and proofs. For most students, it could be skipped or skimmed.
1) Regular languages - this chapter is an introduction to deterministic and nondeterministic finite automata and regular expressions.
2) Context-free languages - an introduction to context-free grammars and pushdown automata.
3) The Church-Turing theses - an introduction to Turing machines and the variants, such as multiple tapes and nondeterministic Turing machines.
4) Decidability - the definition of decidability and how Turing machines and finite automata are used to prove or disprove if a language is decidable.
5) Reducibility - the definition of reducible and how Turing machines can be used to execute reductions.
6) The recursion theorem - an introduction to the recursion theorem and some applications to formal theories.
7) Time complexity - the first chapter in the coverage of algorithmic complexity, in this case execution time.
8) Space complexity - an examination of the complexity of algorithms from the perspective of the amount of memory required.
9) Intractability - an examination of the problems that can be solved in principle but not in practice.
10) Advanced topics in complexity theory - approximation algorithms, probabilistic algorithms, alternation, interactive proof systems, parallel computation and cryptography.
There is less coverage of grammars than most books, which is replaced by more in the area of algorithmic analysis. In my opinion, that is an appropriate tradeoff, the analysis of algorithms gives the students some understanding of how automata are applied in computer science.
Another excellent feature of this book is the solutions to selected exercises that appear at the end of the chapters. My estimate is that reasonably detailed solutions to approximately one-third of the problems are included. This allows the students to work extra problems by themselves, and helps the instructor if they are asked to do another example in class that they have not already worked through.
The exposition is very good; I am convinced that the students will be able to read the material on their own, which is one more reason why I adopted this book for my course.
well-organized, progressive, and understandable.......2007-01-06
As an intro to the theoretical background to computer science goes, this book is about as readable and approachable as you can get.
It gives a very thorough treatment of the whole theoretical basis, from regular languages and pumping lemmas out through Turing machines and related issues, and on to some interesting language classes (like NP and PSpace-complete).
If there's a single sticking point with the book, it's that it insists on a very strict formalism (ie: everything is proof-based) -- something necessary for the topic, but it sometimes renders the material a bit hard to digest.
Great book on the subject.......2006-12-27
If you are interested in or for other reasons must read a book on this subject, this is the book. I took a class last semester which used Hopcroft as the text and I found myself often turning to this book for better understanding. This book is more intuitive and thus a bit less formal than Hopcroft but when trying to learn, understanding is better than mathematical formalism. If you are new to the subject, Sipser is the book to begin with.
Very readable, diverse, and a little sparse.......2006-11-25
This is a wonderful little gem of a book that presents the theory of computation in a fascinating way. It is targeted at advanced undergraduates in computer science, but assumes remarkably little prior knowledge, making it accessible to nearly anyone. The book covers a lot of ground, including the standard fare of automata, computability, and complexity results, plus some bonus material such as probablistic and parallel complexity, information theory, decidable logical theories, and other topics that are normally left out of introductory books. On top of this, the book is remarkably thin!
The best attribute of Sipser's book, though, is the engaging style. This is an easy book to read. You will not feel like you're running into a brick wall, as is sometimes the case with books on abstract topics. It's not so much that the book is slow or gentle (it's really not) as that it is interesting, engaging, and has a knack for stopping short of getting too caught up in details. A number of small things -- the occasional amusing exercise, the "proof idea" sections, or helpful pictures -- add up to an enjoyable reading experience.
Two cautions are appropriate to students considering this book. First, there are variations between authors in the definitions of various automata (especially PDAs). The differences are trivial, and more a matter of taste than of any real importance; but it could come up if you use Sipser as a supplement to a course that follows a different textbook. Second, the coverage of many topics in Sipser's book is brief and concise, sometimes more than you might like. Some important concepts (for example, pairwise distinguishability of strings) are only mentioned in exercises, not in the main chapter, so at least skim all the exercises even if you don't do them. The sketchy coverage is especially pronounced in advanced topics, so (as always) expect to do some filling in of concepts if you go on into further study of this area.
Most appropriate for CS students.......2006-06-01
As a teacher of the subject, I have had the chance to evaluate numerous books on the theory of computation. Of all the available texts, I think this one is the most appropriate for CS students. In the past I taught out of Dexter Kozen's book, which is incredibly elegant, but had some resistance from the students. Thinking it over I decided that Kozen's text, although beautiful, may be better suited to students pursuing a degree in pure math. Sipser's book, on the other hand, is more gentle. I find that Sipser demands far less mathematical maturity from his readers, and thus allows the difficulty to be shifted from excessive formalism to the inherent challenges present in the material. In addition, following Sipser's treatment, I was able to cover finite state machines and pushdown automata in far less time, thus allowing me to concentrate on computability and beyond. The book really shines in its treatment of computability theory, eloquently directing attention to some of the most beautiful aspects.
Another benefit of Sipser's book is the exercises, of which there are many more in this edition. Someone studying on their own should find the initial group of exercises in each section quite approachable. Even the more challenging problems are not incredibly hard, and typically draw their difficulty from the deeper themes of the chapter instead of obscure details.
If you are looking for an enjoyable, well-paced book with an introduction to computability and complexity that is truly inspiring, this is the one for you. A mathematician looking for a bit more rigor may do better with Kozen.
Book Description
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface
Download Description
Like the popular first edition, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction, including a fully-revised Chapter 8 and 30 new technique sections, is a fully functional platform-independent Java software s
Customer Reviews:
Customer Satisfaction.......2007-09-07
The book I got was in very good condition and the time it took for delivery was also good
A great introduction to data mining and machine learning.......2007-07-24
I bought this book in the hopes that it would help me better explore the data from the Netflix Prize contest, which it did. I had been reading numerous Wikipedia articles, scientific papers, etc. on line and felt it would be useful to have a more general tome on the subject. This book covers many of the common, overarching themes i.e. clustering, neural networks, linear regression, etc. to varing degree. I only wish the examples involved slightly more complex data sets and more pseudo code was provided. I suppose since the book is very closely tied to WEKA, one could always dig through the source code of that application; but I feel that the authors could have provided a bit more of the strictly algorithm relevant code in the book.
Incredibly Useful for theory and Practice.......2007-05-15
This book is by the guys who created WEKA. It covers a wide swath of machine learning without losing a dilligent reader. Then it walks the reader through using WEKA - a fantastically powerful open source tool.
Amazing.
wrong named.......2007-04-20
If "practical" means having a sample project with the theory, the name of this book is incorrectly named. With a big bunch of text or description, it is not "practical"!
Incredibly practical introduction.......2006-10-30
This book is perfect if you are trying to get your hands around what data mining and machine learning is. Most of the books I have read on this subject want to start with equations and get more complex from there, with little practicality. This book makes extensive use of examples and introduces the mathematical basis for algorithms where needed. The authors make the point that simpler algoritms often work best for solving machine learning problems. Similarly, I would argue, simpler books work best for understanding highly complex fields. I very highly recommend this book.
Book Description
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful
An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.
Customer Reviews:
Great statistics book........2007-09-24
I'm a machine learning person, and this book provides pretty thorough state-of-art and up-to-date (relatively well) summary of statistical methods being used in lots of pattern classification fields. One thing that does not exist in the book is generative models, although this book is the best of the kind that describes discriminitive models.
Most Useful Machine Learning Book.......2007-09-24
This book describes most of the important topics in machine learning. Most machine learning books just present a criterion and and an optimization algorithm. For instance, LDA is often presented as: here is the Fisher criterion, it seems like a good thing to maximize. "The Elements of Statistical Learning" also presents that this is the right criterion if the distributions of the data for each class are Gaussian with the same covariance. This book puts all the algorithms in the same statistical language, which makes them easy to compare and choose between.
I also appreciate the emphasis this book puts on algorithms that are more recently popular/effective. I very much appreciate the discussions of logistic regression vs. LDA, ridge and lasso regression, boosting/additive logistic regression and additive trees, decision and regression trees, ...
The only qualm I have with this book is that it is rather biased toward the authors' own research. It is difficult from reading this book alone to differentiate between classical techniques and the authors' recent proposed algorithms.
Best data mining book.......2007-09-21
If you are looking for a relatively rigorous but very readable data mining book, this is simply the best! It covers most of the modern techniques and is beautifully printed with high quality graphics.
A very introductory book and well-writen.......2007-02-05
The discussed book is very explanatory and could be students' material for academic lessons.
A must book for every statistician and data miner.......2007-01-18
This book has become a classic for any statistician and data miner by now.
It is a broad overview of regression, classification and clustering techniques (supervised and unsupervised machine learning).
Book Description
This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
Customer Reviews:
Outstanding.......2007-09-12
I read this book about 7 years ago while in the PhD program at Stanford University. I consider this book not only the best Machine Learning book, but one of the best books in all of Computer Science. It covers every branch of ML I know of and it covers it really well. I found Mitchell's chapter on Neural Networks more insightful than an entire book on NN's that I read. I also found his chapter on Reinforcement Learning more useful and better explained than an entire book on Reinforcement Learning that I also read. The other chapters cover other ML topics at the same level of quality and rigor.
The author did an amazing job in covering the breadth and depth of ML in less than 500 pages. I hope he will write a new edition to cover the advances that happened in the last decade.
Great Start to Machine Learning.......2007-08-27
I have used this book during my masters and found it to be an extremely helpful and a gentle introduction to the thick and things of machine learning applications. The various chapters are nicely paced with helpful problems at the end. Another great thing about the book is treatment of detailed examples with each concept and that the author carefully ties various concepts as they arise, with not just new, but also examples from previous chapters, which helps the user to understand different concepts applied to same problems thereby making clear difference between different methods. Also the author has a dedicated website with updated errata and notes, which is also very helpful! Having said that, I think the book is an introduction to various machine learning methods and one can easily follow on the references listed for detailed treatment of relevant topics.
Best book I've seen on topic.......2007-01-31
I have this book listed as one of the best and most interesting I've ever read. I loved the book just as much as I loved the course we used it in.
I have a genuine interest in AI and especially Machine Learning and this book both inspired me as well as clared some things up for me. Like how the spectrum of different known methods differ in their appoach of different problems.
This book is also very concise and well written, without being too mathematical. Making it very easy to read and understand.
Ever since I took that course I keep returning to this book as a reference when I have a related problem to solve, or just bothering my mind.
Highly recommended!
too expensive I would say.......2006-10-13
great book if you wanna start sth anywhere in machine learning, but it is toooooo expensive.
Excellent book, concise and readable.......2006-06-22
This is a great book if you're starting out with machine learning. It's rare to come across a book like this that is very well written and has technical depth. The writing is to the point, maybe even a bit terse, but all that you need to know is in there. It's a bit old so doesn't cover kernel methods or SVM's but is still a great first machine learning book.
Book Description
Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.
Customer Reviews:
For Advanced Undergrads to Practitioners.......2006-12-23
The amount of textual information floating around on the web is staggering. And the overwhelming amount of this information is simply passed along from sender to receiver for the human being reading it to make sense out of it. There are obvious demands to use a computer to 'read' this material and select out appropriate nuggets from the ore.
Intended for use by advanced undergraduate students, graduate students, researchers and people working in the field, this book first covers the definition of the problem and presents several state-of-the-art probabilistic models for information extraction, and how these models can be used in applications. Finally a rather detailed description of several real life applications are included: patent searching, scanning magazine articles, and scanning the news for business intelligence.
Do you suppose that somewhere, some body (or maybe a lot of bodies) are using these techniques to find words like bomb, explosion, etc.
Book Description
Text Classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn text classifiers from examples. However, none of these conventional approaches combines good prediction performance, theoretical understanding, and efficient training algorithms. Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning. Learning To Classify Text Using Support Vector Machines is designed as a reference for researchers and practitioners, and is suitable as a secondary text for graduate-level students in Computer Science within Machine Learning and Language Technology.
Customer Reviews:
The Gold standard.......2007-05-16
This is a must read for anyone beginning to investigate the analysis of meaning in text using computational methods. I found the initial sections were useful in bringing together my thought on many different aspects of the topic.
Wonderful book on the subject.......2005-09-03
This is a Tesis Work, it contains a review and conmparation of several learners. It focuses mainly on SVM.
Book Description
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
Customer Reviews:
More for mathematicians than computer scientist.......2006-09-20
This book introduces the concepts of kernel-based methods and focuses specifically on Support Vector Machines (SVM). It is hard to read and a good background in mathematic is clearly needed. The book has a strong emphasis on SVM starting from the very first line of text. Concepts are well explained, although equations are not clear. The notation doesn't facilitate the reading at all. The book covers linear as well as kernel learning. The kernel trick is well described. It is easy to understand ideas behind SVM while reading the corresponding chapter. Finally a small chapter on SVM applications is proposed. Unfortunately, it only contains typical SVM applications (i.e. standard problems).
I think this book is good if you:
* Have a strong mathematical background
* Work in the specific domain of SVM (or kernel-based methods in general)
* Want to write a research paper about SVM and need the correct notations
However, this book is NOT intended for people who:
* Don't like to read theorems, corollaries and remarks
* Are not interested in reading hundreds of proofs
This is my personal opinion as a computer scientist: this book is definitely written for mathematicians.
A little dry........2006-01-09
The book is a little dry at times. Also, I didn't get a very clear idea of how to select kernel functions, which seems pretty important.
Not even close to an intro..........2004-03-21
Oh Puhleeeezzzzz... How is your vector math??? Remember your linear algebra well? Do you have a background in SVM's? Intuitively able to suck out of thin air the meaning of the Gamma co-efficient as applied to svm's?? You've read all the background papers and remember your formal logic???? No?? too bad..your out of luck..
This book is more aptly titled an Introduction to the Formalisms of SVM's. If your a software engineer trying to implement one of these, forget it.. Be nice if they put that quadratic algorthim psuedocode into something more readable than greek symbology..
If you are trying to build one of these engines, then this book is of absolutely no help, unless you have a background in machine learning and have read all the papers on SVM's. If you can decompose the math into code in your head, then you might find it entertaining... What I don't get is how all the rest of these reviewers can give such "glowing praise" for this book and have it be so completely worthless as an introduction... makes me think some of these are shills..
Bottom line is, if your trying to code a svm, this book will not help. If your trying to understand how to implement a svm, this book will not help. If you are trying to understand how an svm works, this book will not help. If you want to know the mathematical basis for SVM's and like that presentation.. this is the book for you..
Excellent book.......2003-11-19
I just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John Shawe-Taylor. Could not resist adding my own comments about the book. Excellent book. I plan to use the book for the course on "Fundamentals of computer aided engineering" that I teach at the Swiss Federal Institute of Technology, Lausanne (EPFL).
This is it !.......2001-08-31
The book is just great. The appendix on algorithms could have more explanations. Also the application section is a short. It would have been more usuful to take one of these applicaitons and describe it in details. But all in all, the book is excellent.
Book Description
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
Customer Reviews:
An extremely good book.......2006-11-16
This is a very good book. Another reviewer has commented on Vojislav Kecman being an excellent teacher. I whole-heartedly second that opinion. Often times, while reading this book, you will pause with a doubt or question. What you will find surprising is that almost certainly the author has answered that question in the next paragraph. Many times, the author's answers will tally your own answers.
The first chapter of the book (entitled: Learning and Soft Computing: Rationale, Motivations, Needs, Basics) is 119 pages long. It is an essential reading. By the time you finish reading this chapter the things will start falling into place and you will be more motivated and ready to read the remaining chapters. Until you are highly aware of this topic, do not skip this chapter.
A book is made up of a lot of things other than the text that it covers. Does it contain many/any stupid jokes? Is it printed on the highest quality paper? Is the font size good? Is it printed too dense? Is the cover page inviting enough? Are the dimensions/weight of the book correct? On all these counts the book scores high.
Consistent with the subject matter that it covers, this is not an easy book. You will perhaps like to read it with paper and pencil. But if you are willing to spend time with this book, this book will do a lot of good to you. This is a very good book.
An excellent book on Machine Learning.......2003-02-27
What strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.
This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.
I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.
Excellent, useful book!.......2001-07-23
This book is a nice and, I would say, a successful attempt to provide a unified survey of important theoretical and practical machine learning tools: neural networks (NN), support vector machines (SVM) and fuzzy systems (FS).
Book consists of nine chapters, covering SVMs, one- and multi-layer perceptrons and radial-basis function networks, as variants of neural networks, and basics of fuzzy theory. This is followed by interesting case-studies (in financial, control and computer graphic applications) and concluded by basics of optimization theory and an overview of necessary mathematical tools. All the MATLAB programs needed for the simulated experiments are available on the book web site.
Authored by Vojislav Kecman, a prominent researcher in the field of soft computing and previous MIT visiting professor, this book is an excellent material for advanced undergraduate and introductory graduate courses in machine learning applications and soft computing....
Book Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.
Customer Reviews:
Good Theoretical GA Textbook.......2005-05-06
This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.
There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.
To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.
Not for beginners.......2004-02-04
I have an engineering degree, and I found this to be a little tough to follow for two reasons:
1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.
2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.
When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!
An introduction and much more.......2004-01-26
First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.
Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.
The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.
A Great Introduction to Genetic Algorithms.......2002-12-07
This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.
About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.
Good introduction for such a short book.......2002-04-07
Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.
Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.
The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.
Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.
Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.
Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.
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