Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Simple Decomposition Method for Support Vector Machines
Machine Learning
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
The cross entropy method for classification
ICML '05 Proceedings of the 22nd international conference on Machine learning
Application of the cross-entropy method to clustering and vector quantization
Journal of Global Optimization
Application of the Cross Entropy Method to the Credit Risk Assessment in an Early Warning System
ISIP '08 Proceedings of the 2008 International Symposiums on Information Processing
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In this paper, cross entropy method is used for solving dual Lagrange support vector machine (SVM). Cross entropy (CE) method is a new practical approach which is widely used in some applications such as combinatorial optimization, learning algorithm and simulation. Our approach refers to Kernel Adatron which is solving dual Lagrange SVM using gradient ascent method. Hereby, the cross entropy method is applied to solve dual Lagrange SVM optimization problem to find the optimal or at least near optimal Lagrange multipliers as a solution. As known, the standard SVM with quadratic programming solver suffers from high computational time. Some real world datasets are used to test the algorithms and compare to the existing approach in terms of computation time and accuracy. Our approach is fast and produce good results in terms of generalization error.