Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Online Least Squares Support Vector Machines Based on Wavelet and Its Applications
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Error tolerance based support vector machine for regression
Neurocomputing
Reducing the effect of out-voting problem in ensemble based incremental support vector machines
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Online support vector regression for system identification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Concept updating with support vector machines
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our method exploits the "locality" of RBF kernels to update current machine by only considering a subset of support candidates in the neighbourhood of the input. The determination of this subset is conditioned by the computation of the variation of the error estimate. Implementation is based on the SMO one, introduced and developed by Platt [13]. We study the behaviour of the algorithm during learning when using different generalization error estimates. Experiments on three data sets (batch problems transformed into on-line ones) have been conducted and analyzed.