Data Complexity in Pattern Recognition

Basu, Mitra; Ho, Tin Kam

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.

Details

Published by: Springer

Publication Date: 2010-10-22

Format: Paperback

ISBN-13: 9781849965576

DOI: 10.1007/978-1-84628-172-3

Dimensions: 235.0cm x155.0cm

Pages: 300.0