The multi-category support vector machine (MC-SVM) is one of the most popular learning algorithms. There are numerous MC-SVM variants, although different optimization algorithms were developed for diverse machines. In this study, we a new algorithm that can be applied to several variants. based on Frank-Wolfe framework requires two subproblems, direction-finding and line search, in each iterati...

Support Vector Machines (SVM) is among the most popular classification techniques in machine learning, hence designing fast primal SVM algorithms for large-scale datasets is a hot topic in recent years. This paper presents a new L2norm regularized primal SVM solver using Augmented Lagrange Multipliers, with linear computational cost for Lp-norm loss functions. The most computationally intensive...

The network malicious information filtering is a binary classification problem. The SVM (Support Vector Machine) algorithm can be used in such information filtering model, but the negative samples were difficult to gain in such practical application, so the sample space is imbalance for SVM, the prediction result of classifier would tend to majority class and the filtering error is larger. This...

The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by usin...

Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper...

Nowadays new peer to peer (P2P) traffic with dynamic port and encrypted technology makes the identification of P2P traffic become more and more difficult. As one of the optimal classifiers, support vector machine (SVM) has special advantages with avoiding local optimum, overcoming dimension disaster, resolving small samples and high dimension for P2P classification problems. However, to employ ...

Support Vector Machine (SVM) is a powerful classification and regression tool. Varying approaches including SVM based techniques are proposed for email classification. Automated email classification according to messages or user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text machine learning research. This paper prese...

Gene selection is a key research issue in molecular cancer classification and identification of cancer biomarkers using microarray data. Support vector machine recursive feature elimination (SVM-RFE) is a well known algorithm for this purpose. In this study, a novel gene selection algorithm is proposed to enhance the SVM-RFE method. The proposed approach is designed to use the combination of SV...

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find...