Data Complexity in Pattern Recognition

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Mitra Basu, Tin Kam Ho
Springer Science & Business Media, Dec 22, 2006 - Computers - 300 pages
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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.

 

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Contents

Measures of Geometrical Complexity in Classification Problems
3
Object Representation Sample Size and Data Set Complexity
25
Measures of Data and Classifier Complexity and the Training Sample Size
59
Linear Separability in Descent Procedures for Linear Classifiers
69
Data Complexity MarginBased Learning and Poppers Philosophy of Inductive Learning
91
Data Complexity and Evolutionary Learning
115
Classifier Domains of Competence in Data Complexity Space
135
Data Complexity Issues in Grammatical Inference
153
Simple Statistics for Complex Feature Spaces
173
Polynomial Time Complexity Graph Distance Computation for Web Content Mining
196
Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles
217
Complexity of Magnetic Resonance Spectrum Classification
240
Data Complexity in Tropical Cyclone Positioning and Classification
249
HumanComputer Interaction for Complex Pattern Recognition Problems
271
Complex Image Recognition and Web Security
287
Index
299

Applications
170

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