Model selection plays an important role in cost-sensitive SVM (CS-SVM). It has been proven
that the global minimum cross validation (CV) error can be efficiently computed based on the …

Abstract Model selection is an important problem of costsensitive SVM (CS-SVM). Although
using solution path to find global optimal parameters is a powerful method for model …

Robust support vector machine (RSVM) using ramp loss provides a better generalization
performance than traditional support vector machine (SVM) using hinge loss. However, the …

Semi-supervised support vector machine (S VM) is important because it can use plentiful
unlabeled data to improve the generalization accuracy of traditional SVMs. In order to …

Abstract Model selection with cross validation (CV) is very popular in machine learning.
However, CV with grid and other common search strategies cannot guarantee to find the …

It is well known that the performance of a kernel method is highly dependent on the choice of
kernel parameter. However, existing kernel path algorithms are limited to plain support …

The v-support vector classification (v-SVC) proposed by Schölkopf has the advantage of
using a regularization parameter v for controlling the number of support vectors and margin …

Planetary gear is the key part of the transmission system for large complex
electromechanical equipment, and in general, a series of degradation states are undergone …

This paper proposes a novel ridge-adding-based approach for handling singularities that
are frequently encountered in the powerful SVMpath algorithm. Unlike the existing method …

Abstract Model selection with cross validation (CV) is very popular in machine learning.
However, CV with grid and other common search strategies cannot guarantee to find the …