Introduction to Predictive Learning
Springer (Publisher)
Published on 1. May 2010
Book
Hardback
395 pages
978-1-4419-0258-0 (ISBN)
Description
This textbook offers a non-mathematical approach to predictive learning, emphasizing methodology and principles. It describes conceptual and philosophical aspects of predictive learning, exploring constructive learning algorithms in a coherent framework. The book includes: concepts, such as complexity control, generalization, and basic modeling approaches;philosophical principles of statistical estimation and machine learning;a presentation of statistical learning theory, a framework for learning algorithms;data-analytic methods; neural network and machine learning methodsnon-standard learning methodologies and their SVM-like mathematical description.This book provides a solid methodologies and practical applications for students and practitioners alike. Exercises range from trivial programming to open-ended research questions. Supplemental material includes a solutions manual, lecture slides, data sets, software implementation, and MATLAB scripts.
More details
Edition
2010
Language
English
Place of publication
New York, NY
United States
Target group
Professional and scholarly
Lower undergraduate
Illustrations
100 s/w Abbildungen, 100 farbige Abbildungen
100 black & white illustrations, 100 colour illustrations
Dimensions
Height: 235 mm
Width: 155 mm
ISBN-13
978-1-4419-0258-0 (9781441902580)
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Schweitzer Classification
Content
Introduction.- Basic Learning Approaches and Complexity Control.- Philosophical Perspective.- Philosophical Interpretation of Predictive Learning.- Inductive Learning and Statistical Learning Theory.- Nonlinear Statistical Methods.- Neural Network Learning.- Margin-Based Methods and Support Vector Machines.- Combining Methods and Boosting.- Alternative Learning Formulations.- Appendix A: Probability and Statistics.- Appendix B: Linear Algebra.- Index.