Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
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

Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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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.

Luminous Landscapes: Quilted Visions in Paint and Thread

Average customer rating:

A luminous book
Excellent Resource
love this book

Luminous Landscapes: Quilted Visions in Paint and Thread
Gloria Loughman
Manufacturer: C&T Publishing
ProductGroup: Book
Binding: Paperback

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Release Date: 2007-06-12

Book Description

Capture the glory of a sunrise or sunset, the stark outline of a tree against the winter sky, the glassy surface of the ocean. Luminous Landscapes presents an exciting repertoire of techniques to let crafters turn their own photographs into unique and impressive landscape quilts. Step-by-step techniques and three starter projects guide readers every inch of the way, from taking photos to painting fabric, creating silhouettes, constructing backgrounds, and embellishing.

Customer Reviews:

A luminous book.......2007-08-23

This book is one of the best I've seen. There are some well written and well-organised sections dealing with the principles of composition, colour and stitching techniques. Gloria usually starts her landscapes with a painted background, so she gives a short well-illustrated section on fabric painting. Most of that stuff is available elsewhere, but it is assembled and illustrated particularly well.
The second half of the book contains details of her construction methods. One of those is using freezer paper on the right side of the fabric for appliqued shapes. The second - her signature style - is pieced sections on point, blended into the applique design. She gives clear diagrams
and inspiring examples of this technique. She then gives some examples of how to achieve different effects for trees, water and reflections. There is a gallery of student work, and two fully illustrated projects with paper patterns included at the back.
In my view this is excellent value, a perfect blend of practical tips and inspiration.

Excellent Resource.......2007-08-11

If you love Gloria's quilts you will love this book. I have done two workshops with Gloria and it is very useful to have this book to put everything she covered in the workshops and more as a permanent resource. It is very detailed so is wonderful for people who have not had the opportunity to attend a workshop.

love this book.......2007-07-10

I have several books on art quilting, and they all have their good points, but this book covers so much, I couldn't believe it. It includes, design, color theory, construction, fabric painting, special effects, material lists, and so on. The only thing it doesn't really cover is the use of printed commercial fabrics( there are other books out there for that). I was impressed by the clear instructions and photographs. It even has step-by- step projects in the back of the book and a large pull-out pattern sheet. This book has been an excellent addition to my quilt book how-to/reference library. I wouldn't hesitate to recommend Luminous Landscapes to anyone who is looking for a concise, clearly illustrated book on quilt making with an artistic flair. Great price too!

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

Average customer rating:

Needs a second volume which explains the first
I looked for
The a good introduction to NLP, but could be improved
Good oveview, slightly overrated: broad and shallow
Good, but many errors

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
Daniel Jurafsky , and James H. Martin
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Paperback

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Book Description

This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora. Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

Customer Reviews:

Needs a second volume which explains the first.......2005-05-20

This book is by now an accepted classic in the field. It is basically the only textbook that covers so much of computational linguistics, so I have had no choice but to use it for the past several years. Just the same, I'd rather not use it for teaching linguistics students. While the book has much to offer the professional, including a broad range of topics extensively researched, it is much more useful in this "handbook" capacity than as a textbook for the uninitiated. The chief reasons for this are: 1) It is pedagogically very poor; the majority of concepts are either explained in a confusing and obfuscatory manner or are not explained and are simply left in algorithmic form. This is not usually edifying to the linguistics student with no computer science background. 2) There are too many mistakes in its algorithms and method overviews. So far as I can see, even the famed Earley parsing algorithm is wrong here, it will not yield the correct output. 3) It is not written in a language that linguistics students can understand. With no background in mathematics, computer science, or pseudocode, such students need much more coddling than is provided by this book, and they are virtually unable to read it. Basically, as the title to this review states, what is called for now is a book to explain the contents of this book. Perhaps if my students keep encouraging me to write it. . .

I looked for.......2003-11-06

something which I can use - I am a linguist - and found it immensly readable and useful

The a good introduction to NLP, but could be improved.......2003-04-16

This book helped me accomplish what I set out to do; namely to obtain an overview of the field of natural language processing, with an emphasis on language understanding (as opposed to recognition). And I can recommend it on that level. The weakness of the book however is that it left me asking, "OK, now what?". The book started off strong with a number of dynamic-programming algorithms, finite automaton models, and N-grams that one could sink his/her teeth into from an algorithmic point-of-view. But when it came to actual techniques for natural-language understanding (chapters 14-17) the goods were not delivered. The algorithms disappeared, and the best I could find was in Chapter 15 an incomplete, and unconvincing treatment of Hiyan Alshawi's semantic parsing techniques which fueled the Core Language Engine last decade. Chapter 16 dealt with lexical semantics and was almost entirely devoid of algorithms.

My gut feeling after reading this text is that parsing techniques will likely give way to statistical and probabilistic learning methods that will in some sense bypass the need to correctly or accurately parse language. I cannot fault the authors for not exploring this in more depth,as this represents the cutting edge for both NLP and artificial intelligence. In any case, I'm off to read Schutze and Manning's book which will hopefully provide a bit more focus on that perspective. What intrigues me is that most people can understand some language, but very few people understand the grammar of their own language, especially if they have been deprived of a formal education. So why should computers need to know all about grammar rules and parsing? Could they instead be trained by simply being exposed to enough interactions between language and objects? I teach in a department dominated by both foreign and immigrant students. I understand them most of the time, but I would estimate that half the time their sentences or utterances would not fail to be parsed correctly.

Good oveview, slightly overrated: broad and shallow.......2002-05-26

GENERAL IDEA: Broad coverage, it lacks depth and details - particularly practical details. That is, the presentation is often sketchy, mainly because it approaches too many subjects for its available space. I would not say that this book is strong on theory either. It is quite obvious that it avoids getting too formal and precise, probably to remain attractive for non-specialists too.

CASE STUDY: One specific problem I had with the Hidden Markov Models, that are supperficially presented (or spread I could say) in several separate sections of the book, so it's not been a pleasure trying to actually understand them properly and completely as a fundamental concept, to make them work in my particular application.

TITLE: The book's title IS misleading because it starts with "Speeech" and this book's main subject is not speech but (written) language. Actually there are only a few chapters on speech.

CONCLUSION: Get this book if you are looking for a good overview of the field. The book will introduce you to a thousand of topics. As soon as you need in-depth coverage of some particular topic, you will look for additional resources.

Good, but many errors.......2002-05-20

This book is a great general introduction to NLP, covering a broad range of topics. Unfortunately there are many errors in the mathematical formulae and the algorithm descriptions, so do make sure to download the errata list from the book's home page.

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The Vision Machine (Perspectives)
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Manufacturer: Indiana University Press
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The last decade has seen a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wearable/wireless devices for clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices. Several promising prototypes are emerging for managing patients with debilitating neurological disorders and for monitoring of patients with chronic cardiac diseases. Despite the technological developments of sensing and monitoring devices, issues related to system integration, sensor miniaturization, low-power sensor interface circuitry design, wireless telemetric links and signal processing have still to be investigated. Moreover, issues related to Quality of Service, security, multi-sensory data fusion, and decision support are active research topics.

This book addresses the issues of this rapidly changing field of wireless wearable and implantable sensors and discusses the latest technological developments and clinical applications of body-sensor networks.

Computational Vision in Neural and Machine Systems

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Computational Vision in Neural and Machine Systems

Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0521862604

Book Description

Computational vision deals with the underlying mathematical and computational models for how visual information is processed. Whether the processing is biological or machine, there are fundamental questions related to how the information is processed. How should information be represented? How should information be transduced in order to highlight features of interest while suppressing noise and other artefacts of the image capture process? Computational Vision in Neural and Machine Systems address these and other questions in 13 chapters, divided into three sections, which overlap between biological and computational systems: dynamical systems; attention, motion, and eye-movements; and stereovision. The editors have brought together the best and brightest minds in the field of computational vision, combining research from both biology and computing and enhancing the developing synergy between computational and biological visual modelling communities. Aimed at researchers and graduate students in computational or biological vision, neuroscience, and psychology.

Average customer rating:

nice introduction to topic for computer science and stats
Broadly Useful Reference For Intellignet Data Analysis

Intelligent Data Analysis

Manufacturer: Springer
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ASIN: 3540430601

Book Description

This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.

Customer Reviews:

nice introduction to topic for computer science and stats.......2001-05-06

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

Broadly Useful Reference For Intellignet Data Analysis.......2000-03-06

This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.

The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.

Book Description

With the demands of quality management and process control in an industrial environment machine vision is becoming an important issue. This handbook of machine vision is written by experts from leading companies in this field. It goes through all aspects of image acquisition and image processing. From the viewpoint of the industrial application the authors also elucidate in topics like illumination or camera calibration. Attention is paid to all hardware aspects, starting from lenses and camera systems to camera-computer interfaces. Besides the detailed hardware descriptions the necessary software is discussed with equal profoundness. This includes sections on digital image basics as well as image analysis and image processing. Finally the user is introduced to general aspects of industrial applications of machine vision, such as case studies and strategies for the conception of complete machine vision systems. With this handbook the reader will be enabled not only to understand up to date systems for machine vision but will also be qualified for the planning and evaluation of such technology.

Customer Reviews:

Right amount of detail.......2007-03-06

This is a great book to cover the basics. You won't find advanced techniques here, but will get a good understanding of the eye, cameras, optics, lighting. Covers more recent cameras, buses. You will come away with a thorough understanding of machine vision. Not enough to build your own system from scratch but enough to make some good decisions.

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Machine Vision
Wesley E. Snyder , and Hairong Qi
Manufacturer: Cambridge University Press
ProductGroup: Book
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Providing all the necessary theoretical tools, this comprehensive introduction to machine vision shows how these tools are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises giving insights into the development of practical image processing algorithms. A CD-ROM containing software and data used in these exercises is also included. Aimed at graduate students in electrical engineering, computer science, and mathematics, the book will be a useful reference for professionals as well.

Close Range Photogrammetry and Machine Vision

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Close Range Photogrammetry and Machine Vision
K. B. Atkinson
Manufacturer: Whittles Publishing
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Close Range Photogrammetry: Principles, Techniques and Applications
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ASIN: 1870325737

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