Abstract The ν-Support Vector Regression (ν-SVR) is an effective regression learning
algorithm, which has the advantage of using a parameter ν on controlling the number of …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

We propose a multiple incremental decremental algorithm of support vector machines
(SVM). In online learning, we need to update the trained model when some new …

Convex optimization is at the core of many of today's analysis tools for large datasets, and in
particular machine learning methods. In this thesis we will study the general setting of …

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 …

In this paper we study a class of regularized kernel methods for multi-output learning which
are based on filtering the spectrum of the kernel matrix. The considered methods include …

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 …

This paper proposes a new recurrent model, known as the locally recurrent fuzzy neural
network with support vector regression (LRFNN-SVR), that handles problems with temporal …

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

We propose a multiple incremental decremental algorithm of Support Vector Machine
(SVM). Conventional single cremental decremental SVM can update the trained model …