Title :
A study on SMO-type decomposition methods for support vector machines
fDate :
7/1/2006 12:00:00 AM
Abstract :
Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This paper studies sequential minimal optimization type decomposition methods under a general and flexible way of choosing the two-element working set. The main results include: 1) a simple asymptotic convergence proof, 2) a general explanation of the shrinking and caching techniques, and 3) the linear convergence of the methods. Extensions to some support vector machine variants are also discussed.
Keywords :
convergence; learning (artificial intelligence); optimisation; support vector machines; SMO-type decomposition methods; caching technique; linear convergence; sequential minimal optimization type decomposition methods; shrinking technique; simple asymptotic convergence proof; support vector machines training; working set selections; Computer science; Convergence; Iterative methods; Kernel; Matrix decomposition; Optimization methods; Support vector machines; Symmetric matrices; Upper bound; Decomposition method; sequential minimal optimization (SMO); support vector machine (SVM);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.875973
