Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A novel SVM Geometric Algorithm based on Reduced Convex Hulls
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
A Geometric Nearest Point Algorithm for the Efficient Solution of the SVM Classification Task
IEEE Transactions on Neural Networks
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Geometric methods are very intuitive and provide a theoretical foundation to many optimization problems in the fields of pattern recognition and machine learning. In this brief, the notion of scaled convex hull (SCH) is defined and a set of theoretical results are exploited to support it. These results allow the existing nearest point algorithms to be directly applied to solve both the separable and nonseparable classification problems successfully and efficiently. Then, the popular S-K algorithm has been presented to solve the nonseparable problems in the context of the SCH framework. The theoretical analysis and some experiments show that the proposed method may achieve better performance than the state-of-the-art methods in terms of the number of kernel evaluations and the execution time.