Book Description
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Customer Reviews:
Great product & service.......2007-09-21
This was my first purchase from amazon and I was totally impressed by the quality of the product and the service! I would buy again from the same seller and recommend others to do the same.
A Very Bad Sequel.......2007-03-09
I have now used this book 3 times for a class. While the 1st edition did a nice job of covering the material in its time, the additions to in the 2nd addition are a disaster. What the book has going for it is that it at least lists the necessary material for such a course in the table of contents. However, all the additional material is poorly explained at best. The problem sets are too few and the ones that are included are generally weak.
I have tried to use this book, but after constant student complaints and my own difficulty with the text, I have finally concluded that the problem lies with the text and not with the users.
I think an indicator of problems was the large number of errors in the first printing; large here is an understatement. Even in later additions, the 4th, the size of the errata is huge. I think this is indicative of the authors' attention to detail and seriousness in preparation. I have found similar errors and ambiguities in the associate Computer Manual.
The bottom line is that this book has seen its final appearance in our curriculum. I would use any other text, even an older one.
There is simply not enough room or time to point out all the problems with this text. Do yourself a favor if considering this text for a class. Don't bother.
The best book for the discussed field.......2007-02-05
The discussed book is very explanatory and could be students' material for academic lessons.
great book.......2007-01-16
easy to read for computer scientists who are not necessarily experts in statistics. the code in matlab is very good, and helps a lot.
this book is a good introduction to machine learning.
Very well written.......2006-02-26
I liked this book because it does a great job explaining the concepts and the reasoning behind the mathematical formulae. Other books such as "The Elements of Statistical Learning" toss the Math formulas at you and expect you to figure out the significance or the importance of 'em. The book does not shy away from Math - but does a great job presenting it.
Book Description
Computer Manual to Accompany Pattern Classification and its associated MATLAB software is an excellent companion to Duda: Pattern Classfication, 2nd ed, (DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook.
The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code.
Customer Reviews:
Underwhelmed.......2007-04-04
Talk about over-hype from reviewer #1!
This "manual" is thin on substantive content, with TONS of whitespace & whitepages to stretch it out to ~125pages. The documentation of the code should be available as a PDF with files on MATLAB's file exchange or on the publisher's website. Save yourself some $$.
Excellent toolbox to learn & use........2004-07-09
I was one of the early access recipient of this toolbox and found it extremely useful. It basically has a whole bunch of cleaning and classification algos.
The toolbox also allows one to extend its use with new algorithms, tweaks or to use our dataset. As long as its formatted in the same fashion.
I would strongly recommend this toolbox, if you are looking for additional material, another book worth having is Christopher Bishop's book.
Average customer rating:
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Data Streams: Models and Algorithms (Advances in Database Systems)
Manufacturer: Springer
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Data Streams: Algorithms and Applications (Foundations and Trends in Theoretical Computer Science,)
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Data Mining and Bioinformatics: First International Workshop, VDMB 2006, Seoul, Korea, September 11, 2006, Revised Selected Papers (Lecture Notes in Computer Science)
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Biological and Medical Data Analysis: 7th International Symposium, ISBMDA 2006, Thessaloniki, Greece, December 7-8, 2006. Proceedings (Lecture Notes in Computer Science)
ASIN: 0387287590 |
Book Description
Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.
This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions.
Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
Customer Reviews:
Data Stream Mining book.......2007-02-14
This book is about mining data streams (or data stream mining). Its title is not very accurate but if you see its TOC you will easily understand.
It covers the main areas of data stream mining and is accurate and informative.
Recommended if you are interested in data streams or data stream mining or mining data streams.
PS. There are some small typos at the first 30 pages of the book.
Book Description
This book presents innovative techniques in Recognition and Classification of Astrophysical and Medical Images. The contents include: Introduction to pattern recognition and classification in astrophysical and medical images; Image standardization and enhancement; Region-based methods for pattern recognition in medical and astrophysical images; Advanced information processing using statistical methods; Feature recognition and classification using spectral method.
The book is intended for astrophysicists, medical researches, engineers, research students and technically aware managers in the Universities, Astrophysical Observatories, Medical Research Centres working on the processing of large archives of astrophysical or medical digital images. This book can be used as a text book for students of Computing, Cybernetics, Applied Mathematics and Astrophysics.
Book Description
The chest x-ray is the most commonly performed diagnostic x-ray examination. Highly illustrated and with only a minimum of necessary text, this new book takes a highly efficient pattern-based approach to evaluating the chest x-ray. Classes of findings, such as "increased radiolucency" or "alveolar shadow: atelectasis," are used to orient the reader toward the underlying medical problem. Tables help to organize the basic findings so as to be able to arrive at a differential diagnosis.
While the emphasis is on the chest x-ray, a supporting role is played by helpful CT images, schematics and drawings, and photographs of pathologic specimens, where these may be helpful to understand the chest x-ray appearance of the disease.
Chest x-rays are often performed routinely, prior to employment, prior to surgery or during immigration. The examining physician must be in a position to evaluate large numbers of chest x-rays confidently and speedily. This book succeeds admirably in helping the examiner to this end.
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- Understand clusters and clustering deeply
- different methods for finding clusters
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Classification, Clustering and Data Analysis
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Cluster Analysis
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Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), ... Data Analysis, and Knowledge Organization)
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Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics)
ASIN: 354043691X |
Book Description
This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.
Customer Reviews:
Understand clusters and clustering deeply.......2006-08-19
This is a good and broad approach about cluster and clustering. It is better for those who want to understand deeply the theme. Is has lot of formulas and mathmatics.
different methods for finding clusters.......2005-01-13
The book has a nice treatment of the problem of finding, in some sense, clusters in data. Several papers point out that there is often some subjectivity here, as to which data sits in a particular cluster. Fuzziness in the boundary of a cluster. It can depend on what your underlying model is.
Possibly of interest to some is work on high dimensionality data, and trying to find clusters in these. Even visualisations might be non-trivial.
The book has value in letting you see a variety of ideas for finding clusters. Perhaps some of these might prove germane to your research.
Average customer rating:
- Classification Methods for Remotely Sensed Data
|
Classification Methods for Remote Sensed Data
Paul Mather , and
Brandt Tso
Manufacturer: CRC
ProductGroup: Book
Binding: Paperback
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ASIN: 0415259096 |
Book Description
Remote sensing is an integral part of geography, GIS and cartography, used by academics in the field and professionals in all sorts of occupations. The 1990s saw the development of a range of new methods of classifying remote sensing images and data, both optical imaging and microwave imaging. This comprehensive survey of the various techniques pulls together information from a range of sources and sets it in the context of the basic principles. There is an emphasis on new methods, including neural networks (especially artificial neural networks), fuzzy theory, texture and quantization, and the use of Markov random fields. Students in GIS and remote sensing should find this an essential read when learning about and dealing with new developments in the field. It is concise and accessible and the authors conclude with coverage of the state-of-the-art topics of multisource data analysis, evidential reasoning and genetic algorithms. Including a full color section and basic remote sensing theory, this book will prove invaluable for advanced undergraduate students and graduates/researchers in the field. There is very little published in this field yet, and there is distinct need for such an analysis of this fast-growing area.
Customer Reviews:
Classification Methods for Remotely Sensed Data.......2002-07-02
...I must say that it is the best book on remote sensing I have ever come across...
Customer Reviews:
great though dated reference to pattern recognition.......2000-08-04
This book was published in 1973 and there have been many advances since. Still I find it provides great exposition of the fundamental concepts. In fact the nearest neighbor algorithms that are now popular are covered in this book and date back to the work of Cover and Hart in the late 1960s. Those new to pattern recognition who think kth nearest neighbor rules are new should read this book to find out exactly when it was really thought up.
For a more up-to-date treatment, see McLachlan's recent book in the Wiley statistics series. However, this book provides valuable explanations of Bayes rules and shows pictorially what the boundaries look like for linear and quadratic classifiers. In fact I borrowed their pictures in Chapter 2 of my book on bootstrap methods.
great though dated reference to pattern recognition.......2000-08-04
This book was published in 1973 and there have been many advances since. Still I find it provides great exposition of the fundamental concepts. In fact the nearest neighbor algorithms that are now popular are covered in this book and date back to the work of Cover and Hart in the late 1960s. Those new to pattern recognition who think kth nearest neighbor rules are new should read this book to find out exactly when it was really thought up.
For a more up-to-date treatment, see McLachlan's recent book in the Wiley statistics series. However, this book provides valuable explanations of Bayes rules and shows pictorially what the boundaries look like for linear and quadratic classifiers. In fact I borrowed their pictures in Chapter 2 of my book on bootstrap methods.
Gotta have it.......1997-02-11
While it's becoming a little dated (no problem, v.2 is in the works), this is the "must have" book for anyone concerned with pattern classification and scene analysis - not just computer vision, but anything related to pattern classification. It introduces all of the basic techniques and approaches that anyone involved in data reduction and classification should be familiar with: contour search, interval splines, chain encoding, the effects of digitizing the input data, pattern representation and classification, etc.
Get it. You'll be glad you did.
-Drew
Book Description
Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects.
The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness.
This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.
Customer Reviews:
better ways to classify data?.......2006-05-22
When you have data that is present in some n-dimensional space, you often want to make clusters. The problem is that most methods have a subjective component. What is a cluster is sometimes a matter of definition, within a given method. Clusters can also be used to try to draw up regions of that n-dimensional space. This constitutes a classification of future data. Well, how to do so?
Abe explains an idea that has gained recognition recently. The concept of support vector machines. The label is perhaps a little clumsy. But Abe's book gives a good geometric understanding of current classification ideas and their limitations. And how these can be overcome using support vector machines.
Several variants are explored. Along with a tie-in to neural networks for training. The computations can be intensive for real data. But these days, that is less and less of a limitation.
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Advances in Biometrics: International Conference, ICB 2006, Hong Kong, China, January 5-7, 2006, Proceedings (Lecture Notes in Computer Science)
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback
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ASIN: 3540311114 |
Book Description
This book constitutes the refereed proceedings of the International Conference on Biometrics, ICB 2006, held in Hong Kong, China in January 2006.
The 104 revised full papers presented were carefully reviewed and selected from 192 submissions. Biometric criteria covered by the papers are assigned to face, fingerprint, iris, speech and signature, biometric fusion and performance evaluation, gait, keystrokes, and others. In addition the results of the Face Authentication Competition (FAC 2006) are also announced in this volume.
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Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition
The Elements of Statistical Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Machine Learning (Mcgraw-Hill International Edit)
