DocumentCode
352493
Title
Towards an incremental SVM for regression
Author
Carozza, Menita ; Rampone, Salvatore
Author_Institution
Fac. di Sci. MM.FF.NN., Sannio Univ., Benevento, Italy
Volume
6
fYear
2000
fDate
2000
Firstpage
405
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.859429
Filename
859429
Link To Document