Data Complexity in Pattern Recognition

Herausgegeben:Basu, Mitra; Ho, Tin Kam

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Advanced Information and Knowledge Processing
• Verlag: Springer / Springer London / Springer, Berlin
• Artikelnr. des Verlages: 978-1-84996-557-6
• Softcover reprint of hardcover 1st ed. 2006
• Seitenzahl: 316
• Erscheinungstermin: 22. Oktober 2010
• Englisch
• Abmessung: 235mm x 155mm x 18mm
• Gewicht: 460g
• ISBN-13: 9781849965576
• ISBN-10: 1849965579
• Artikelnr.: 32203520

Inhaltsangabe

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.