We offer an efficient method to reduce the number of support vectors for Fuzzy Support Vector Machine. Firstly, we consider the Fuzzy Support Vector Machine model which was proposed by Lin and Wang. For the reducing the number of support vectors, we apply the l0 regularization term to the dual form of this model. The resulting optimization problem is non-smooth and non-convex. The l0 is then re...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

Healthcare and disease detection in early stage is important every human being. Proper optimum of with smart controller done using Particle swarm optimization (PSO) Support Vector Machine (SVM). The research includes the Fuzzy Proportional Integral Derivative (Fuzzy PID) was used support vector machine to classify heart disease. Swarm Optimization designed remove noise introduced Electrocardiog...

Fuzzification of support vector machine has been utilized to deal with outlier and noise problem. This importance is achieved, by the means of fuzzy membership function, which is generally built based on the distance of the points to the class centroid. The focus of this research is twofold. Firstly, by taking the advantage of robust statistics in the fuzzy SVM, more emphasis on reducing the im...

Power system stabilizers (PSSs) are used to enhance the damping during low frequency oscillations. Artificial intelligence techniques provide one alternative for stability enhancement and speed deviation (Δw). In this paper we have applied Fuzzy based and Support Vector Machine (SVM) based approach to PSS for Single Machine Infinite Bus (SMIB) System .The proposed method using SVM techniques ac...

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable...

Support vector machines (SVMs) proved to be highly efficient computational tools in various classification tasks. However, SVMs are nonlinear classifiers and the knowledge learned by an SVM is encoded in a long list of parameter values, making it difficult to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy–rule ...

Traditional support vector machines (SVMs) assign data points to one of two classes, represented in the pattern space by two disjoint half-spaces. In this paper, we propose a fuzzy extension to proximal SVMs, where a fuzzy membership is assigned to each pattern, and points are classi1ed by assigning them to the nearest of two parallel planes that are kept as distant from each other as possible....

Support Vector Machines (SVM) have demonstrated their ability in solving classification problems in an optimal way with a solid mathematical background. In this paper we improve the interpretability of SVM’s by showing that every SVM is exactly represented by a Fuzzy Rule BasedSystem, for every kernel function used. Nevertheless, this system is in some way compact in their rules and for that re...