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Feature Extraction

Foundations and Applications

  • Book
  • © 2006

Overview

Editors:
  1. Isabelle Guyon

    1. Clopinet, Berkeley, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Masoud Nikravesh

    1. Department of Electrical Engineering & Computer Science — EECS, University of California, Berkeley, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Steve Gunn

    1. School of Electronics and Computer Sciences, University of Southampton, Southampton Highfield, UK

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    You can also search for this editor in PubMed   Google Scholar

  4. Lotfi A. Zadeh

    1. Division of Computer Science Lab. Electronics Research, University of California, Berkeley, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Features the results of the NIPS 2003 workshop on feature extraction
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 207)

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Table of contents (32 chapters)

  1. Front Matter

    Pages I-XXIV
    Download chapter PDF

  2. An Introduction to Feature Extraction

    1. An Introduction to Feature Extraction

      • Isabelle Guyon, André Elisseeff
      Pages 1-25

  3. Feature Extraction Fundamentals

    1. Front Matter

      Pages 27-27
      Download chapter PDF

    2. Learning Machines

      • Norbert Jankowski, Krzysztof Grabczewski
      Pages 29-64

    3. Assessment Methods

      • Gérard Dreyfus, Isabelle Guyon
      Pages 65-88

    4. Filter Methods

      • Włodzisław Duch
      Pages 89-117

    5. Search Strategies

      • Juha Reunanen
      Pages 119-136

    6. Embedded Methods

      • Thomas Navin Lal, Olivier Chapelle, Jason Weston, André Elisseeff
      Pages 137-165

    7. Information-Theoretic Methods

      • Kari Torkkola
      Pages 167-185

    8. Ensemble Learning

      • Eugene Tuv
      Pages 187-204

    9. Fuzzy Neural Networks

      • Madan M. Gupta, Noriyasu Homma, Zeng-Guang Hou
      Pages 205-233

  4. Feature Selection Challenge

    1. Front Matter

      Pages 235-235
      Download chapter PDF

    2. Design and Analysis of the NIPS2003 Challenge

      • Isabelle Guyon, Steve Gunn, Asa Ben Hur, Gideon Dror
      Pages 237-263

    3. High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees

      • Radford M. Neal, Jianguo Zhang
      Pages 265-296

    4. Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems

      • Kari Torkkola, Eugene Tuv
      Pages 297-313

    5. Combining SVMs with Various Feature Selection Strategies

      • Yi-Wei Chen, Chih-Jen Lin
      Pages 315-324

    6. Feature Selection with Transductive Support Vector Machines

      • Zhili Wu, Chunhung Li
      Pages 325-341

    7. Variable Selection using Correlation and Single Variable Classifier Methods: Applications

      • Amir Reza Saffari Azar Alamdari
      Pages 343-358

    8. Tree-Based Ensembles with Dynamic Soft Feature Selection

      • Alexander Borisov, Victor Eruhimov, Eugene Tuv
      Pages 359-374

    9. Sparse, Flexible and Efficient Modeling using L 1 Regularization

      • Saharon Rosset, Ji Zhu
      Pages 375-394
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Keywords

About this book

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).

Editors and Affiliations

  • Clopinet, Berkeley, USA

    Isabelle Guyon

  • Department of Electrical Engineering & Computer Science — EECS, University of California, Berkeley, USA

    Masoud Nikravesh

  • School of Electronics and Computer Sciences, University of Southampton, Southampton Highfield, UK

    Steve Gunn

  • Division of Computer Science Lab. Electronics Research, University of California, Berkeley, USA

    Lotfi A. Zadeh

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