A new training method for support vector machines: Clustering k-NN support vector machines
Küçük Resim Yok
Tarih
2008
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
For training of support vector machines (SVMs) efficiently, a new training algorithm, clustering k-NN (k-nearest neighbor) support vector machines (CKSVMs) based on a Gaussian function regulated locally is proposed. In order to reflect degree of training data point as a support vector the Gaussian function is used with k-nearest neighbor (k-NN) method and Euclidean Distance measure. To add local control property to the training algorithm, a simple clustering scheme is implemented before Gaussian functions are constructed for each cluster. In addition, probabilistic SVM outputs are used for extension from binary classification to multi-class classification in pairwise approach. This training algorithm is applied to three commonly used classification problems. Experimental results show that the CKSVM has more classification accuracy than standard multi-class LS-SVM, FLS-SVM and LS-SVM with k-NN method which is proposed in our previous study. In addition to this, the training algorithm highly improved efficiency of the SVM classifier via simple algorithm. (c) 2007 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
support vector machines, least squares support vector machines, Gaussian functions, k-nearest neighbor, probabilistic outputs
Kaynak
EXPERT SYSTEMS WITH APPLICATIONS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
35
Sayı
3
