Dynamically adapting kernels in support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Gradient-Based Adaptation of General Gaussian Kernels
Neural Computation
Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features
Proceedings of the 2006 ACM symposium on Applied computing
PSO and multi-funnel landscapes: how cooperation might limit exploration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multiclass SVM Model Selection Using Particle Swarm Optimization
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
International Journal of Knowledge-based and Intelligent Engineering Systems
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Combining Meta-learning and Search Techniques to SVM Parameter Selection
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
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Support Vector Machines (SVMs) have become a well succeed algorithm due to the good performance it achieves on different learning problems. However, to perform well the SVM formulation requires adjustments on its model. Avoiding the trial and error procedure, the automatic SVM parameter selection is a way to deal with this. The automatic parameter selection is commonly considered an optimization problem whose goal is to find suitable configuration of parameters which attends some learning problem. In the current work, we propose a study of the combination of Meta-learning (ML) with Particle Swarm Optimization (PSO) algorithms to optimize the SVM model, seeking for combinations of parameters which maximize the success rate of SVM. ML is used to recommend SVM parameters, to a given input problem, based on well-succeeded parameters adopted in previous similar problems. In this combination, initial solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of candidate search points, in the search process, to find an adequate solution, would be less expensive. In our work, we implemented five benchmarks PSO approaches applied to select two SVM parameters for classification. The experiments consist in comparing the performance of the search algorithms using a traditional random initialization and using ML suggestions as initial population. This research analysed the influence of meta-learning on convergence of the optimization algorithms, verifying that the combination of PSO techniques with ML obtained solutions with higher quality on a set of 40 classification problems.