Data Complexity in Pattern Recognition (eBook)

Mitra Basu, Tin Kam Ho (Herausgeber)

eBook Download: PDF

2006
XVI, 300 Seiten
Springer London (Verlag)
978-1-84628-172-3 (ISBN)

Infos

Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.

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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.

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.

Produkt-Details

• Erscheint lt. Verlag: 22.12.2006
• Reihe/Serie: Advanced Information and Knowledge Processing
• Reihe/Serie: Advanced Information and Knowledge Processing
• Zusatzinfo: XVI, 300 p.
• Verlagsort: London
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Informatik ► Theorie / Studium ► Algorithmen
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: algorithm • Algorithm analysis and problem complexity • algorithms • classification • Clustering • Cognition • Complexity • Data Complexity • Discrimination Analysis • Evolution • Graph • Human-Computer Interaction (HCI) • Knowledge • learning • Neural networks • Pattern • pattern recognition • Philosophy
• ISBN-10: 1-84628-172-5 / 1846281725
• ISBN-13: 978-1-84628-172-3 / 9781846281723