Title :
On data based learning using support vector clustering
Author_Institution :
Dept. of Informatics Eng., Coimbra Univ., Portugal
Abstract :
This paper addresses the effect of applying clustering algorithms, based on a distance metric rule, prior to support kernel learning in classification and regression problems. Self-Organising Maps (SOMs), which place emphasis in data domain description, and K-means clustering algorithms have been selected before applying a support vector algorithm which is based on a margin rule. Moreover, the recently developed support vector clustering algorithm, based on a cluster boundary rule, is applied in benchmark problems for comparison.
Keywords :
learning (artificial intelligence); pattern classification; pattern clustering; regression analysis; self-organising feature maps; support vector machines; time series; Box Jenkins furnace time series prediction; K-means clustering; benchmark problems; classification problems; cluster boundary rule; clustering algorithms; computational learning; data based learning; distance metric rule; margin rule; regression problems; self-organising maps; support vector clustering; two spirals data set problem; Algorithm design and analysis; Clustering algorithms; Informatics; Kernel; Machine learning; Mathematics; Parametric statistics; Pattern recognition; Self organizing feature maps; Support vector machines;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1201948
