Pairwise Multi-classification Support Vector Machines: Quadratic Programming (QP-PAMSVM) formulations
نویسنده
چکیده
The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-class problem. We propose a single quadratic optimization problem called a pairwise multi-classification support vector machines (PAMSVMs) for constructing a pairwise linear and nonlinear classification decision functions. A kernel approach is also discussed for nonlinear classification problems. Computational results are presented for two real data sets.
منابع مشابه
An unified framework for 'All data at once' multi-class Support Vector Machines
Support Vectors (SV) are a machine learning procedure based on Vapnik’s Statistical Learning Theory, initially defined for bi-classification problems. A lot of work is being made from different research areas to obtain new algorithms for multi-class problems, the more usual task in real-world problems. A promising extension is to treat ‘all data at once’ into one multi-class SVM by modifying th...
متن کاملParallel Decomposition Approaches for Training Support Vector Machines
We consider parallel decomposition techniques for solving the large quadratic programming (QP) problems arising in training support vector machines. A recent technique is improved by introducing an efficient solver for the inner QP subproblems and a preprocessing step useful to hot start the decomposition strategy. The effectiveness of the proposed improvements is evaluated by solving large-sca...
متن کاملA 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...
متن کاملActive-Set Methods for Support Vector Machines
This chapter describes an active-set algorithm for the solution of quadratic programming problems in the context of Support Vector Machines (SVMs). Most of the common SVM optimizers implement working-set algorithms like the SMO method because of their ability to handle large data sets. Although they show generally good results, they may perform weakly in some situations, e.g., if the problem is...
متن کاملSequential minimal optimization: A fast Algorithm for Training Support Vector machines
This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consu...
متن کامل