Data Complexity in Pattern Recognition

Tin Kam Ho

ISBN 10: 1849965579 / ISBN 13: 9781849965576

Published by Springer London, Springer London Okt 2010, 2010

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Machines capable of automatic pattern recognition have many fascinating uses in science & engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability.This book takes a close view of data complexity & its role in shaping the theories & techniques in different disciplines & asks:What is missing from current classification techniques When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data Uunique in its comprehensive coverage & multidisciplinary approach from various methodological & practical perspectives, researchers & practitioners will find this book an insightful reference to learn about current available techniques as well as application areas.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 316 pp. Englisch.

Seller Inventory # 9781849965576

Bibliographic Details

• Title: Data Complexity in Pattern Recognition
• Publisher: Springer London, Springer London Okt 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 1849965579
• ISBN 13: 9781849965576
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

A unique book for researchers and practitioners across disciplines (including Computer Science, Engineering, Social Science and Finance) to learn about the current status, available techniques, and applications of data complexity in pattern recognition.

From the Back Cover

Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach.

This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks:

• What is missing from current classification techniques?

• When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task?

• How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data?

Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.