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Demand-Driven Associative Classification

By Adriano Veloso , Wagner Meira Jr.

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This volume focuses on a major machine learning task known as classification. Some classification problems are hard to solve, but this book shows that they can be decomposed into much simpler sub-problems.

Full Description

  • ISBN13: 978-0-8572-9524-8
  • 125 Pages
  • User Level: Science
  • Publication Date: May 18, 2011
  • Available eBook Formats: PDF
  • eBook Price: $39.95
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Full Description
The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.
Table of Contents

Table of Contents

  1. Introduction and Preliminaries.
  2. Introduction.
  3. The Classification Problem.
  4. Associative Classification.
  5. Demand
  6. Driven Associative Classification.
  7. Extensions to Associative Classification.
  8. Multi
  9. Label Associative Classification.
  10. Competence
  11. Conscious Associative Classification.
  12. Calibrated Associative Classification.
  13. Self
  14. Training Associative Classification.
  15. Ordinal Regression and Ranking.
  16.  Conclusions and FutureWork.
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