Although the twin support vector regression (TSVR) method has been widely studied and various variants are successfully developed, the structural risk ...
Jun 19, 2018 · Although the twin support vector regression (TSVR) method has been widely studied and various variants are successfully developed, ...
Numerical experiments on diverse datasets have verified the effectiveness of our proposed NPSVR in sparseness, generalization ability and scalability.
This work constructs two nonparallel hyperplanes in such a way that each one is closer to one of the training patterns, and as far as possible from the ...
Although the twin support vector regression (TSVR) method has been widely studied and various variants are successfully developed, the structural risk ...
TL;DR: Two novel active learning approaches based on non-parallel support vector machines and twin support vector Machines which adopt the margin sampling ...
Sequential minimal optimization (SMO) is the most popular approach for solving SVM problems[4]. SMO performs a series of two-point optimizations. In each ...
At each iteration, an SMO algorithm requires the solution of a convex quadratic programming of two variables with one linear equality constraint and box ...
Decomposition methods are currently one of the major methods for training support vector ma- chines. They vary mainly according to different working set ...
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The proposed SMO-like algorithm generalizes the classical SMO algorithm, designed for solving QP problems, to tackle more general nonsmooth, nonlinear convex ...