The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Mercer’s theorem, feature maps, and smoothing
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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The aim of the research reported is to propose a training algorithm for support vector machine based on kernel functions and to test its performance in case of non-linearly separable data. The training is based on the Sequential Minimal Optimization introduced by J.C. Platt in 1999. The final section of the paper presents a series of the results obtained in testing the performance of the proposed algorithm on samples randomly generated from Gaussian two-dimensional distributions, and on data available in Wisconsin Diagnostic Breast Cancer Database.