Title :
Towards an incremental SVM for regression
Author :
Carozza, Menita ; Rampone, Salvatore
Author_Institution :
Fac. di Sci. MM.FF.NN., Sannio Univ., Benevento, Italy
Abstract :
We propose an incremental support vector machine (SVM) approach to regularization. Support vectors are added in an iterative manner during the training process. For each new vector added, the kernel parameters are settled according to an extended chained version of the Nadaraja-Watson estimator. We show this approach minimize the expected risk and leads to an efficient learning procedure
Keywords :
estimation theory; function approximation; learning (artificial intelligence); neural nets; optimisation; Nadaraja-Watson estimator; function approximation; iterative; learning procedure; optimisation; regression; support vector machine; Additive noise; Gaussian noise; Kernel; Machine learning; Noise generators; Probability; Support vector machine classification; Support vector machines; Training data; Upper bound;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859429
