The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
On domain knowledge and feature selection using a support vector machine
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Learning to Trade with Incremental Support Vector Regression Experts
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
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Support vector machine (SVM) provides good generalization performance but suffers from a large amount of computation. This paper presents an incremental learning strategy for support vector regression (SVR). The new method firstly formulates an explicit expression of ||W||2 by constructing an orthogonal basis in feature space together with a basic Hilbert space identity, and then finds the regression function through minimizing the formula of ||W||2 rather than solving a convex programming problem. Particularly, we combine the minimization of ||W||2 with kernel selection that can lead to good generalization performance. The presented method not only provides a novel way for incremental SVR learning, but opens an opportunity for model selection of SVR as well. An artificial data set, a benchmark data set and a real-world data set are employed to evaluate the method. The simulations support the feasibility and effectiveness of the proposed approach.