Feature Extraction Foundations and Applications 1st Edition by Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A Zadeh ISBN 3540354883 9783540354888 by Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh 9783540354871, 3540354875 instant download after payment.

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Product details:

ISBN 10: 3540354883

ISBN 13: 9783540354888 

Author: Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh 

Everyonelovesagoodcompetition. AsIwritethis,twobillionfansareeagerly anticipating the 2006 World Cup. Meanwhile, a fan base that is somewhat smaller (but presumably includes you, dear reader) is equally eager to read all about the results of the NIPS 2003 Feature Selection Challenge, contained herein. Fans of Radford Neal and Jianguo Zhang (or of Bayesian neural n- works and Dirichlet di?usion trees) are gloating “I told you so” and looking forproofthattheirwinwasnota?uke. Butthematterisbynomeanssettled, and fans of SVMs are shouting “wait ’til next year!” You know this book is a bit more edgy than your standard academic treatise as soon as you see the dedication: “To our friends and foes. ” Competition breeds improvement. Fifty years ago, the champion in 100m butter?yswimmingwas22percentslowerthantoday’schampion;thewomen’s marathon champion from just 30 years ago was 26 percent slower. Who knows how much better our machine learning algorithms would be today if Turing in 1950 had proposed an e?ective competition rather than his elusive Test? But what makes an e?ective competition? The ?eld of Speech Recognition hashadNIST-runcompetitionssince1988;errorrateshavebeenreducedbya factorofthreeormore,butthe?eldhasnotyethadtheimpactexpectedofit. Information Retrieval has had its TREC competition since 1992; progress has been steady and refugees from the competition have played important roles in the hundred-billion-dollar search industry. Robotics has had the DARPA Grand Challenge for only two years, but in that time we have seen the results go from complete failure to resounding success (although it may have helped that the second year’s course was somewhat easier than the ?rst’s). 

Table of contents:

1. An Introduction to Feature Extraction

2. Assessment Methods

3. Filter Methods

4. Combining a Filter Method with SVMs

5. Information Gain Correlation and Support Vector

6. Combining Information-Based Supervised

7. An Input Variable Importance Definition

8. Ensemble Learning

9. Ensembles of Regularized Least Squares Classifiers

10. Combining SVMs with Various Feature Selection

11. Variable Selection using Correlation and Single Variable

12. Tree-Based Ensembles with Dynamic Soft Feature

13. Sparse Flexible and Efficient Modeling

14. Margin Based Feature Selection and Infogain

15. Nonlinear Feature Selection with the Potential Support

16. Constructing Orthogonal Latent Features

17. Highly Predictive Features

18. Elementary Statistics

19. Confidence Intervals

20. ARCENE

21. GISETTE

22. DOROTHEA

23. MATLAB Code of the Lambda Method

24. High Dimensional Classification with Bayesian Neural

25. Index

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Tags: Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A Zadeh, Feature, Extraction, Foundations, Applications