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Author: David G. Stork
ISBN : B002LA09W6
New from $87.86
Format: PDF
You can download Free Pattern Classification (2nd Edition): Pattern Classification Pt.1 [Kindle Edition] for everyone book 4shared, mediafire, hotfile, and mirror link 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.Books with free ebook downloads available Free Pattern Classification : Pattern Classification Pt.1
- File Size: 7919 KB
- Print Length: 680 pages
- Publisher: Wiley; 2 edition (October 1, 2000)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B002LA09W6
- Text-to-Speech: Enabled
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- Lending: Enabled
- Amazon Best Sellers Rank: #330,855 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Engineering > Electrical & Electronics > Digital Design - #100
in Books > Computers & Technology > Programming > Algorithms > Pattern Recognition
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in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Engineering > Electrical & Electronics > Digital Design - #100
in Books > Computers & Technology > Programming > Algorithms > Pattern Recognition
Free Pattern Classification : Pattern Classification Pt.1
This book is a revised edition of Duda and Hart's classic text on Pattern Classification which was originally published in 1973. In fact, the 1973 edition of the book played a pivotal role in introducing me (and countless researchers of my generation) to the field of pattern classification. Needless to say, I was looking forward to the release of the revised edition. Unfortunately, I was extremely disappointed with the new edition. I had expected much more from the masters: Duda and Hart!My reasons for disappointment with this book are as follows:
Given the 27 years that have elapsed since the publication of the first edition of the book, and the immense progress that has taken place in pattern recognition, machine learning, computational learning theory, grammar inference, statistical inference, algorithmic information theory, and related areas, the revisions and additions in the 2000 edition are essentially of a patchwork nature. In my opinion, they do not reflect the current understanding of the topic of pattern classification.
A disproportionate number of pages are devoted to topics like density estimation despite the fact that it has been well established in recent years, through the work of Vapnik and others, that when working with limited data, trying to solve the problem of pattern classification through density estimation (which turns out to be, in a well-defined sense of the term, a much harder problem than pattern classification) is rather futile. When modern techniques for learning pattern classifiers from limited data sets (e.g., support vector classifiers) are touched on in the book, the treatment is disappointingly superficial and in some cases, misleading.
The 1973 edition of Pattern Classification by Richard Duda and Peter Hart is one of the most cited books in the fields of image processing, machine vision, and classification. It contains perhaps the clearest, most comprehensible descriptions of statistical inference ever written. Though intended for the image processing audience, it is general in its approach, and is broader in coverage than other contemporary books like the redoubtable Van Trees (1969). The section on Bayesian Learning anticipates the EM algorithm which appeared a few years later (Dempster, et al. 1977) and their description of Parzen windows for density estimation is more often cited than Parzen's own papers. The appearance of the 2000 2nd edition led this writer to wonder if D&H could repeat with an offering as good as their first. In particular, would D&H have kept up with the considerable growth in methodology in the 1990s? Well, they have! With the addition of David Stork as third author, the second addition re-presents the basic theory, illustrated with some beautiful and complex figures, and knits it neatly with an exposition of neural networks, stochastic methods for posterior determination, nonmetric classification (tree search and string parsing), and clustering. Chapter 9 is a particularly interesting review of the recent machine learning research making the point that, absent knowledge of a problem's specific domain, no one classifier is better that any other. This chapter also reviews solutions to the problem of training on too-small samples including the Jackknife and bootstrap methods, and newer bagging and boosting algorithms popular in data mining applications.
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