Efficient and interpretable fuzzy classifiers from data with support vector learning
Intelligent Data Analysis
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Stopping conditions for exact computation of leave-one-out error in support vector machines
Proceedings of the 25th international conference on Machine learning
Regularization Paths for ν-SVM and ν-SVR
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm
Neurocomputing
An improved algorithm for the solution of the regularization path of support vector machine
IEEE Transactions on Neural Networks
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
Mining efficient and interpretable fuzzy classifiers from data with support vector learning
ICAI'05/MCBC'05/AMTA'05/MCBE'05 Proceedings of the 6th WSEAS international conference on Automation & information, and 6th WSEAS international conference on mathematics and computers in biology and chemistry, and 6th WSEAS international conference on acoustics and music: theory and applications, and 6th WSEAS international conference on Mathematics and computers in business and economics
A new alpha seeding method for support vector machine training
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Speed-Up LOO-CV with SVM classifier
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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In this paper, we give an efficient method for computing the leave-one-out (LOO) error for support vector machines (SVMs) with Gaussian kernels quite accurately. It is particularly suitable for iterative decomposition methods of solving SVMs. The importance of various steps of the method is illustrated in detail by showing the performance on six benchmark datasets. The new method often leads to speedups of 10-50 times compared to standard LOO error computation. It has good promise for use in hyperparameter tuning and model comparison.