Making large-scale support vector machine learning practical
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
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Heuristic methods for the rejection of noisy training examples in the support vector machine (SVM) are introduced. Rejection of' training errors, either offline or online, results in a sparser model that is less affected by noisy data. A simple offline heuristic provides sparser models with similar generalization performance to the standard SVM, at the expense of longer training times. An online approximation of this heuristic reduces training time and provides a sparser model than the SVM with a slight decrease in generalization perfprmance.