Rule Extraction from Support Vector Machines

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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Diederich, Joachim (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Studies in Computational Intelligence, 80
Temas:
Engineering mathematics.
Engineering-Data processing.
Artificial intelligence.
Mathematical and Computational Engineering Applications.
Artificial Intelligence.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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