The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Electromechanical equipment state forecasting based on genetic algorithm - support vector regression
Expert Systems with Applications: An International Journal
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A new method of reducing the computational load in decision functions provided by a support vector classification machine is studied. The method exploits the geometrical relations when the kernels used are based on distances to obtain bounds of the remaining decision function and avoids to continue calculating kernel operations when there is no chance to change the decision. The method proposed achieves savings in operations of 25-90% whilst keeping the same accuracy. Although the method is explained for support vector machines, it can be applied to any kernel binary classifier that provides a similar evaluation function.