Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Three-dimensional imaging information acquisition system based on DSP and SVM
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A reduced data set method for support vector regression
Expert Systems with Applications: An International Journal
A prototype classifier based on gravitational search algorithm
Applied Soft Computing
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The paper reports on the robust pattern classification of experimental data using a combined approach of symbolization followed by support vector machine (SVM) classification. Symbolization of data removes unwanted features such as noise whereas SVM provides the classification. The SVM parameters are tuned on-line using a genetic-quasi-Newton algorithm. Benchmark examples illustrate the proposed approach.