Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A generalized S-K algorithm for learning v-SVM classifiers
Pattern Recognition Letters
A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
The robust and efficient adaptive normal direction support vector regression
Expert Systems with Applications: An International Journal
GPCA method for fraud detection in mobile communication networks
TELE-INFO'06 Proceedings of the 5th WSEAS international conference on Telecommunications and informatics
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Support Vector Machine (SVM) has become a very effective method in statistical machine learning and it has proved that training SVM is to solve Nearest Point pair Problem (NPP) between two disjoint closed convex sets. Later Keerthi pointed out that it is difficult to apply classical excellent geometric algorithms directly to SVM and so designed a new geometric algorithm for SVM. In this article, a new algorithm for geometrically solving SVM, Kernel Projection Algorithm, is presented based on the theorem on fixed-points of projection mapping. This new algorithm makes it easy to apply classical geometric algorithms to solving SVM and is more understandable than Keerthi's. Experiments show that the new algorithm can also handle large-scale SVM problems. Geometric algorithms for SVM, such as Keerthi's algorithm, require that two closed convex sets be disjoint and otherwise the algorithms are meaningless. In this article, this requirement will be guaranteed in theory by using the theoretic result on universal kernel functions.