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In this paper, we train support vector regressors (SVRs) fusing sequential minimal optimization (SMO) and Newton's method. We use the SVR formulation that includes the absolute variables. A partial derivative of the absolute variable with respect to the associated variable is indefinite when the variable takes on zero. We determine the derivative value according to whether the optimal solution exits...
In our previous work, we proposed feature selection by block addition (BA) and block deletion (BD). In this paper, to further reduce features, we iterate BABD until no features are eliminated. In our method, we add several features at a time to the feature set until a stopping condition is satisfied. Then we delete features that do not deteriorate the selection criterion by block deletion. We iterate...
In this paper, we propose multiple nonlinear subspace methods (MNSMs), in which each class consists of several subspaces with different kernel parameters. For each class and each candidate kernel parameter, we generate the subspace by KPCA, and obtain the projection length of an input vector onto each subspace. Then, for each class, we define the discriminant function by the sum of the weighted lengths...
In our previous work we have developed an active set training method of L2 support vector machines (SVMs) using Newton's method. Because the method allows a solution to be infeasible during training, convergence of the method is not guaranteed. In this paper, we guarantee convergence of active set training by limiting the corrections under the constraints when slow convergence is detected. Namely,...
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