Intelligent Data Analysis: An Introduction / Edition 2

Intelligent Data Analysis: An Introduction / Edition 2

by Michael R. Berthold
     
 

This monograph is a detailed introductory presentation of the key classes of intelligent data analysis (IDA) methods. The 12 coherently written chapters by leading experts provide complete coverage of the core issues.

The previous edition was completely revised and a new chapter on kernel methods and support vector machines and a chapter on visualization

See more details below

Overview

This monograph is a detailed introductory presentation of the key classes of intelligent data analysis (IDA) methods. The 12 coherently written chapters by leading experts provide complete coverage of the core issues.

The previous edition was completely revised and a new chapter on kernel methods and support vector machines and a chapter on visualization techniques were added. The revised chapters from the original edition cover classical statistics issues, ranging from the basic concepts of probability through general notions of inference to advanced multivariate and time-series methods, and provide a detailed discussion of the increasingly important Bayesian approaches. The remaining chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions to the topics of rule induction methods, neural networks, fuzzy logic, and shastic search methods. The book concludes with a higher-level overview of the IDA processes, illustrating the breadth of application of the presented ideas.

The second edition features an extensive index, which makes this volume also useful as a quick reference on the key techniques in intelligent data analysis.

Read More

Product Details

ISBN-13:
9783540430605
Publisher:
Springer Berlin Heidelberg
Publication date:
02/01/2007
Edition description:
2nd rev. and ext. ed. 2003. Corr. 2nd printing 2006
Pages:
515
Product dimensions:
6.00(w) x 9.40(h) x 1.00(d)

Table of Contents

1. Introduction; 2. Statistical Concepts; 3. Statistical Methods; 4. Bayesian Methods; 5. Support Vector and Kernel Methods; 6. Analysis of Time Series; 7. Rule Induction; 8. Neural Networks; 9. Fuzzy Logic; 10. Shastic Search Methods; 11 Visualization; 12. Systems and Applications; Appendix A: Information Theory and Decision Tree Induction; Appendix B: Tools; References; Index; Author Addresses.

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >