Ten Lectures on Statistical and Structural Pattern Recognition
Author:   M.I. Schlesinger ,  Václav Hlavác
Publisher:   Springer
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   24
ISBN:  

9789048160273


Pages:   522
Publication Date:   22 September 2011
Format:   Paperback
Availability:   Out of stock   Availability explained
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Ten Lectures on Statistical and Structural Pattern Recognition


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Overview

This monograph explores the close relationship of various well-known pattern recognition problems that have so far been considered independent. These relationships became apparent with the discovery of formal procedures for addressing known problems and their generalisations. The generalised problem formulations were analysed mathematically and unified algorithms were found. The main scientific contribution of this book is the unification of two main streams in pattern recognition - the statistical one and the structural one. The material is presented in the form of ten lectures, each of which concludes with a discussion with a student. It provides new views and numerous original results in their field. Written in an easily accessible style, it introduces the basic building blocks of pattern recognition, demonstrates the beauty and the pitfalls of scientific research, and encourages good habits in reading mathematical text.

Full Product Details

Author:   M.I. Schlesinger ,  Václav Hlavác
Publisher:   Springer
Imprint:   Springer
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   24
Dimensions:   Width: 15.50cm , Height: 2.80cm , Length: 23.50cm
Weight:   0.831kg
ISBN:  

9789048160273


ISBN 10:   9048160278
Pages:   522
Publication Date:   22 September 2011
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Lecture 1 Bayesian statistical decision making.- Lecture 2 Non-Bayesian statistical decision making.- Lecture 3 Two statistical models of the recognised object.- Lecture 4 Learning in pattern recognition.- Lecture 5 Linear discriminant function.- Lecture 6 Unsupervised learning.- Lecture 7 Mutual relationship of statistical and structural recognition.- Lecture 8 Recognition of Markovian sequences.- Lecture 9 Regular languages and corresponding pattern recognition tasks.- Lecture 10 Context-free languages, their 2-D generalisation, related tasks.

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