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
Tabu Search
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering
Design and application of hybrid intelligent systems
Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features
Proceedings of the 2006 ACM symposium on Applied computing
International Journal of Knowledge-based and Intelligent Engineering Systems
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Using Meta-learning to Classify Traveling Salesman Problems
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Combining Meta-learning and Search Techniques to SVM Parameter Selection
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
| Hi-index | 0.01 |
Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.