Rule Extraction from Support Vector Machines

Rule Extraction from Support Vector Machines edited by Joachim Diederich.

Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a compre...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Diederich, Joachim (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edition:1st ed. 2008.
Series:Studies in Computational Intelligence, 80
Springer eBook Collection.
Subjects:
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Rule Extraction from Support Vector Machines: An Introduction
  • Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring
  • Algorithms and Techniques
  • Rule Extraction for Transfer Learning
  • Rule Extraction from Linear Support Vector Machines via Mathematical Programming
  • Rule Extraction Based on Support and Prototype Vectors
  • SVMT-Rule: Association Rule Mining Over SVM Classification Trees
  • Prototype Rules from SVM
  • Applications
  • Prediction of First-Day Returns of Initial Public Offering in the US Stock Market Using Rule Extraction from Support Vector Machines
  • Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction
  • Rule Extraction from SVM for Protein Structure Prediction.

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