Title :
SVM-KM: speeding SVMs learning with a priori cluster selection and k-means
Author :
De Almeida, Marcelo Barros ; De Padua Braga, Antonio ; Braga, João Pedro
Author_Institution :
Dept. of Electron. Eng., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
Abstract :
A procedure called SVM-KM, based on clustering by k-means and to accelerate the training of support vector machines, is the main objective of the work. During the support vector machines (SVMs) optimization phase, training vectors near the separation margins, are likely to become support vector and must be preserved. Conversely, training vectors far from the margins are not in general taken into account for the SVM´s design process. SVM-KM groups the training vectors in many clusters. Clusters formed only by a vector that belongs to the same class label can be disregard and only cluster centers are used. On the other hand, clusters with more than one class label are unchanged and all training vectors belonging to them are considered. Clusters with mixed composition are likely to happen near the separation margins and they may hold some support vectors. Consequently, the number of vectors in a SVM training is smaller and the training time can be decreased without compromising the generalization capability of the SVM
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); learning automata; pattern classification; pattern clustering; SVM-KM; class label; cluster centers; cluster selection; generalization capability; k-means; optimization phase; separation margins; support vector machines; training time; training vectors; Acceleration; Gene expression; Kernel; Lagrangian functions; Pattern recognition; Process design; Quadratic programming; Support vector machines; Text recognition; Unsolicited electronic mail;
Conference_Titel :
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location :
Rio de Janeiro, RJ
Print_ISBN :
0-7695-0856-1
DOI :
10.1109/SBRN.2000.889732