Description

Book Synopsis
This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.

Table of Contents
Introduction; Representation; Nearest Neighbour Based Classifiers; Bayes Classifier; Decision Trees; Support Vector Machines; Combination of Classifiers; Clustering; Application: Handwritten Digit Recognition; Summary and Conclusions.

Introduction To Pattern Recognition And Machine

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A Hardback by M Narasimha Murty, V Susheela Devi

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    View other formats and editions of Introduction To Pattern Recognition And Machine by M Narasimha Murty

    Publisher: World Scientific Publishing Co Pte Ltd
    Publication Date: 16/06/2015
    ISBN13: 9789814335454, 978-9814335454
    ISBN10: 9814335452
    Also in:
    Pattern recognition

    Description

    Book Synopsis
    This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.

    Table of Contents
    Introduction; Representation; Nearest Neighbour Based Classifiers; Bayes Classifier; Decision Trees; Support Vector Machines; Combination of Classifiers; Clustering; Application: Handwritten Digit Recognition; Summary and Conclusions.

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