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.
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.
Doctoral Thesis / Dissertation from the year 2010 in the subject Computer Science - Applied, grade: none, , course: Department of Computers and Information Sciences - Ph.D., language: English,...
Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support...
Methods exploring the application of support vector machine learning (SVM) to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of...
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting...
Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing,...
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