BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Probabilistic Theory Of Pattern Recognition

Probabilistic Theory Of Pattern Recognition

Probabilistic Theory Of Pattern Recognition
Stock photo: cover may vary

Probabilistic Theory Of Pattern Recognition Hardback - 1997

by Devroye, Luc,

Add to wish list
  • Used
New

Description

like new.
Ask the seller a question Add to wish list
A$233.02
A$5.79 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)

Details

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

Reader reviews for Probabilistic Theory Of Pattern Recognition

From the publisher

Pattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field.

From the rear cover

Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, tree classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material.
tracking-