Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Discovering regression data quality through clustering methods
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
An experimental analysis of the impact of accuracy degradation in SVM classification
International Journal of Computational Intelligence Studies
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We propose a modified SVM algorithm for the classification of data augmented with explicit quality quantification for each example in the training set. As the extension to nonlinear decision functions through the use of kernels brings to a non-convex optimization problem, we develop an approximate solution. Finally, the proposed approach is applied to a set of benchmarks and contrasted with analogous methodologies in the literature.