Rule Extraction from Support Vector Machines

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Diederich, Joachim (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Studies in Computational Intelligence, 80
• Θέματα: 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...

Πίνακας περιεχομένων:

• 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.