A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
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16 of 17 people found the following review helpful:
5.0 out of 5 stars
May be the best pr book from a theoretical standpoint,
By Todd Ebert (Long Beach California) - See all my reviews
This review is from: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)
In giving this book a second read, its importance finally dawned on me: it is one of the few if only books that provides a well-rounded theoretical (i.e. mathematical definitions and proofs) perspective on pattern recognition. Although other books, such as Duda et al's "Pattern Classification", have a significant degree of mathematical rigor, very few can claim to be based on the solid mathematical foundations of Lesbesgue measure theory, as this book is. This book has been a big inspiration for me, in that most pr papers I come across provide some method X, and show how experimentally it is more efficient or effective than methods Y and Z. Such papers, although possibly generating interest in the subject or method, do little if anything to advance the theory which in the end will have the final say of how, when, and why something works or when it doesn't. On the other hand, by making the assumption that the data comes from an unknown (i.e. nonparametric) probability measure space which induces an inherent optimal Bayesian error on the classification problem, this book shows how the theory of probability can be used to prove some very interesting results. As an example, the authors define what it means to have a universally consistent classifier; i.e. a classifier which converges to the optimal Bayesian classifier as the amount of training data approaches infinity in the limit (irregardless of the data distribution). Moreover, one of the important results is that such classifiers exist and are often quite easy to devise (e.g. nearest-neighbor methods). And to be able to mathematically prove this is indeed inspiring. In closing, I would highly recommend this book to anyone who has the mathematical prereqs (probability from an abstract measure-theory point of view) For those who are more interested in the practice of pattern recogition, the above mentioned book by Duda et al. (ISBN 0471056693) will do just fine as a reference. The book "Pattern Recognition" by Theodoridis et al. is also of high quality (ISBN: 0126858756).
12 of 13 people found the following review helpful:
5.0 out of 5 stars
deep and comprehensive,
By A Customer
This review is from: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)
This is an awesome book, the best in-depth book on statistical classification to date. Filled with theorems and proofs on classical nonparametric techniques plus neural networks and learning. Standard reference for anybody doing serious pattern recognition and learning. Destined to become a classical reference in the field.
7 of 7 people found the following review helpful:
5.0 out of 5 stars
An excellent but should be rated R.,
By A Customer
This review is from: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)
The book is great but the notations the authors employ will make you want to drop it on a first reading. Despite the generic title, it is really a reference book for the experts.Issues in generalization are presented better in the book by Anthony and Bartlett but overall it is the best book available (for learning theorists).
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Inside This Book
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First Sentence:
Pattern recognition or discrimination is about guessing or predicting the unknown nature of an observation, a discrete quantity such as black or white, one or zero, sick or healthy, real or fake. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
empirical error minimization, empirical squared error, empirical error probability, regular histogram rule, kernel classification rule, weak universal consistency, strong universal consistency, asymptotic error probability, moving window rules, potential function rules, shatter coefficients, arrangement classifier, generalized linear classifiers, kernel rule, deleted estimate, histogram rules, best linear classifier, holdout estimate, linear discrimination rules, nonparametric rules, regression function estimation, resubstitution estimate, histogram classifiers, estimated error probability, regression function estimate Key Phrases - Capitalized Phrases (CAPs): (learn more)
Use Chernoff, Use Problem, Partitioning Rules Based, Splitting Criteria Based New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Pattern recognition or discrimination is about guessing or predicting the unknown nature of an observation, a discrete quantity such as black or white, one or zero, sick or healthy, real or fake. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
empirical error minimization, empirical squared error, empirical error probability, regular histogram rule, kernel classification rule, weak universal consistency, strong universal consistency, asymptotic error probability, moving window rules, potential function rules, shatter coefficients, arrangement classifier, generalized linear classifiers, kernel rule, deleted estimate, histogram rules, best linear classifier, holdout estimate, linear discrimination rules, nonparametric rules, regression function estimation, resubstitution estimate, histogram classifiers, estimated error probability, regression function estimate Key Phrases - Capitalized Phrases (CAPs): (learn more)
Use Chernoff, Use Problem, Partitioning Rules Based, Splitting Criteria Based New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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