Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Automatic Hyperparameter Tuning for Support Vector Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Multiclass SVM Design and Parameter Selection with Genetic Algorithms
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Multiclass SVM Model Selection Using Particle Swarm Optimization
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Model selection via Genetic Algorithms for RBF networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - SBRN'02
Evolutionary design of multiclass support vector machines
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
An improved parameter tuning method for support vector machines
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm
Neurocomputing
Multi-objective uniform design as a SVM model selection tool for face recognition
Expert Systems with Applications: An International Journal
Introducing the separability matrix for error correcting output codes coding
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Automatically searching for optimal parameter settings using a genetic algorithm
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Expert Systems with Applications: An International Journal
Minimal design of error-correcting output codes
Pattern Recognition Letters
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
Computers in Biology and Medicine
On the design of an ECOC-Compliant Genetic Algorithm
Pattern Recognition
A nested heuristic for parameter tuning in Support Vector Machines
Computers and Operations Research
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Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided into multiple binary subproblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier.