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.

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

Inhalt
Theory and Methodology.- Measures of Geometrical Complexity in Classification Problems.- Object Representation, Sample Size, and Data Set Complexity.- Measures of Data and Classifier Complexity and the Training Sample Size.- Linear Separability in Descent Procedures for Linear Classifiers.- Data Complexity, Margin-Based Learning, and Popper's Philosophy of Inductive Learning.- Data Complexity and Evolutionary Learning.- Classifier Domains of Competence in Data Complexity Space.- Data Complexity Issues in Grammatical Inference.- Applications.- Simple Statistics for Complex Feature Spaces.- Polynomial Time Complexity Graph Distance Computation for Web Content Mining.- Data Complexity in Clustering Analysis of Gene Microarray Expression Profiles.- Complexity of Magnetic Resonance Spectrum Classification.- Data Complexity in Tropical Cyclone Positioning and Classification.- Human-Computer Interaction for Complex Pattern Recognition Problems.- Complex Image Recognition and Web Security.