Comparing Techniques for Multiclass Classification Using Binary SVM Predictors
Lecture notes in computer sciencePublished 1 January 2004
Ana Carolina Lorena, André C. P. L. F. de Carvalho
Citations14
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
Results indicate that the use of Directed Acyclic Graphs is an efficient approach in generating multiclass SVM classifiers.
Abstract
Multiclass classification using Machine Learning techniques consists of inducing a function f(x) from a training set composed of pairs (x i ,y i ) where y i ∈ {1,2,...,k}. Some learning methods are originally binary, being able to realize classifications where k = 2. Among these one can mention Support Vector Machines. This paper presents a comparison of methods for multiclass classification using SVMs. The techniques investigated use strategies of dividing the multiclass problem into binary subproblems and can be extended to other learning techniques. Results indicate that the use of Directed Acyclic Graphs is an efficient approach in generating multiclass SVM classifiers.
Keywords
Computer Science
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