Analysis and classification of technical analysis indicators by support vector machines

ISIS Technical Report Support Vector Machines for Classification and Regression

Analysis of support vector machines

We compare L1 and L2 soft margin support vector machines from the standpoint of positive definiteness, the number of support vectors, and uniqueness and degeneracy of solutions. Since the Hessian matrix of L2 SVMs is positive definite, the number of support vectors for L2 SVMs is larger than or equal to the number of L1 SVMs. For L1 SVMs, if there are plural irreducible sets of support vectors,...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

A QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES

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...

Multicategory Classification by Support Vector Machines

We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik’s Support Vector Machines (SVM) can be combined to yield two ...