The nature of statistical learning theory
The nature of statistical learning theory
An equivalence between sparse approximation and support vector machines
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Training v-support vector regression: theory and algorithms
Neural Computation
Pattern Selection for Support Vector Classifiers
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ϵ-Tube based pattern selection for support vector machines
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Reducing samples for accelerating multikernel semiparametric support vector regression
Expert Systems with Applications: An International Journal
Selecting training points for one-class support vector machines
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
ASVMFC: adaptive support vector machine based fuzzy classifier
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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With increasing of the number of training examples, training time for support vector regression machine augments greatly. In this paper we develop a method to cut the training time by reducing the number of training examples based on the observation that support vector's target value is usually a local extremum or near extremum. The proposed method first extracts extremal examples from the full training set, and then the extracted examples are used to train a support vector regression machine. Numerical results show that the proposed method can reduce training time of support regression machine considerably and the obtained model has comparable generalization capability with that trained on the full training set.