Logistic Regression (LR) has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to Support Vector Machines (SVM) and AdaBoost. In this paper, we use a modified version of LR to approximate the optimization of SVM by a sequence of unconstrained optimization problems. We prove that our approximation wil...

This article presents a solution along with experimental results for an application of semi-supervised machine learning techniques and improvement on the SVM (Support Vector Machine) based on geodesic model to build text classification applications for Vietnamese language. The objective here is to improve the semi-supervised machine learning by replacing the kernel function of SVM using geodesi...

In this paper we construct the linear support vector machine (SVM) based on the nonlinear rescaling (NR) methodology (see [11, 14, 12] and references therein). The formulation of the linear SVM based on the NR method leads to an algorithm which reduces the number of support vectors without compromising the classification performance compared to the linear soft-margin SVM formulation. The NR alg...

Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classi...

In this article we construct a maximal margin classification algorithm for arbitrary metric spaces. At first we show that the Support Vector Machine (SVM) is a maximal margin algorithm for the class of metric spaces where the negative squared distance is conditionally positive definite (CPD). This means that the metric space can be isometrically embedded into a Hilbert space, where one performs...

In this paper we evaluate the performance of the highest probability SVM nearest neighbor classifier, which is a combination of the SVM and k-NN classifiers, on a corpus of email messages. To classify a sample the algorithm performs the following actions: for each k in a predefined set {k1, ..., kN} it trains an SVM model on k nearest labelled samples, and uses this model to classify the given ...

we can reach by dna microarray gene expression to such wealth of information with thousands of variables (genes). analysis of this information can show genetic reasons of disease and tumor differences. in this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...

The improved AdaBoost-SVM algorithm is used to classify the safety and the risk from the Peers-to-Peers net loan platforms. Since the SVM algorithm is hard to deal with the rare samples and its training is slow, rule sampling is used to reduce the classify noise. Then, with the combinations of learning machine, P2P risks can be identified. The result shows that IAdaBoost algorithm can improve t...

In this paper, a fast bounded parametric margin  -support vector machine (BP- SVM) for classification is proposed. Different from the parametric margin  -support vector machine (par- -SVM), the BP- -SVM maximizes a bounded parametric margin, and consequently the successive overrelaxation (SOR) technique could be used to solve our dual problem as opposed solving the standard quadratic progr...

We propose Sparse TSVM, a multi-class SVM classifier that determines k nonparallel planes by solving k related SVM-type problems. The Sparse TSVM promotes Twin SVM to one-versus-rest approach. And it capture classes' main feature better with the sparse algorithm. On several benchmark data sets, Sparse TSVM is not only fast, but shows good generalization.