Overview
- Shows how to appreciate the presence and nature of patterns in specific problems
- Helps the reader set proper expectations for classification performance
- Offers guidance on choosing the best pattern recognition classification techniques
- Interdisciplinary coverage helps the reader absorb and apply useful developments in diverse fields: Engineering, Computer Science, Social Sciences and Finance
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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About this book
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.
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Table of contents (15 chapters)
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Front Matter
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Theory and Methodology
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Front Matter
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Applications
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Front Matter
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Back Matter
Editors and Affiliations
Bibliographic Information
Book Title: Data Complexity in Pattern Recognition
Editors: Mitra Basu, Tin Kam Ho
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-1-84628-172-3
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2006
Hardcover ISBN: 978-1-84628-171-6Published: 17 October 2006
Softcover ISBN: 978-1-84996-557-6Published: 22 October 2010
eBook ISBN: 978-1-84628-172-3Published: 22 December 2006
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XVI, 300
Topics: Pattern Recognition, Artificial Intelligence, Algorithm Analysis and Problem Complexity