Rule Extraction from Support Vector Machines

• Corporate Author: SpringerLink (Online service)
• Other Authors: Diederich, Joachim (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Series: Studies in Computational Intelligence, 80
• Subjects: Engineering.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

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.