A New Smooth Support Vector Machine with 1-Norm Penalty Term

A New Formulation for Cost-Sensitive Two Group Support Vector Machine with Multiple Error Rate

Support vector machine (SVM) is a popular classification technique which classifies data using a max-margin separator hyperplane. The normal vector and bias of the mentioned hyperplane is determined by solving a quadratic model implies that SVM training confronts by an optimization problem. Among of the extensions of SVM, cost-sensitive scheme refers to a model with multiple costs which conside...

The F∞-norm Support Vector Machine

In this paper we propose a new support vector machine (SVM), the F∞-norm SVM, to perform automatic factor selection in classification. The F∞-norm SVM methodology is motivated by the feature selection problem in cases where the input features are generated by factors, and the model is best interpreted in terms of significant factors. This type of problem arises naturally when a set of dummy var...

1-norm Support Vector Machines

The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an ef£cient algorithm that computes the whole solution path of the 1-norm SVM, hence facilitates adaptive selection of...

Robust Support Vector Machine Using Least Median Loss Penalty

It is found that data points used for training may contain outliers that can generate unpredictable disturbance for some Support Vector Machines (SVMs) classification problems. No theoretical limit for such bad influence is held in traditional convex SVM methods. We present a novel robust misclassification penalty function for SVM which is inspired by the concept of “Least Median Regression”. I...

Hidden Space Smooth Support Vector Machine with C Means Clustering