Nowadays, support vector machines (SVM) are receiving increasing attention in land cover/use classification although one of the major drawbacks of the technique is the kernel function selection and its parameters setting. In this paper, a novel SVM parameters optimization method based on selfadaptive mutation particle swarm optimizer (SAMPSO-SVM) is proposed to improve the generalization perfor...

BACKGROUND User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. METHODS We conducted experiments on online user reviews using di...

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combina...

Support vector machine soft margin classifiers are important learning algorithms for classification problems. They can be stated as convex optimization problems and are suitable for a large data setting. Linear programming SVM classifier is specially efficient for very large size samples. But little is known about its convergence, compared with the well understood quadratic programming SVM clas...