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
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support vector machines in data mining
Support vector machines in data mining
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
A study on reduced support vector machines
IEEE Transactions on Neural Networks
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Reduced Support Vector Machines (RSVM) was proposed as the alternate of standard support vector machines (SVM) in order to resolve the difficulty in the learning of nonlinear SVM for large data set problems. RSVM preselects a subset as support vectors and solves a smaller optimization problem, and it performs well with remarkable efficiency on training of SVM for large problem. All the training points of the subset will be support vectors, and more training points are selected into this subset results in high possibility to obtain RSVM with better generalization ability. So we first obtain the RSVM with more support vectors, and selects out training examples near classification hyper plane. Then only these training examples are used as training set to obtain a standard SVM with less support vector than that of RSVM. Computational results show that standard SVMs on the basis of RSVM have much less support vectors and perform equal generalization ability to that of RSVM.