Search
Now showing items 1-2 of 2
Decomposition techniques for support vector machines training and applications.
(2002)
The theory of the Support Vector Machine (SVM) algorithm is based on statistical learning theory and can be applied to pattern recognition and regression. Training of SVMs leads to either a quadratic programming (QP) ...
Incremental kernel learning algorithms and applications.
(2006)
Since the Support Vector Machines (SVMs) were introduced in 1995, SVMs have been recognized as essential tools for pattern classification and function approximation. Numerous publications show that SVMs outperform other ...