A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Using analytic QP and sparseness to speed training of 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
IEEE Intelligent Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Text classification using string kernels
The Journal of Machine Learning Research
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Evolutionary learning with kernels: a generic solution for large margin problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving kernels for support vector machine classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Controlling overfitting with multi-objective support vector machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A model for a complex polynomial SVM kernel
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
A generic multi-dimensional feature extraction method using multiobjective genetic programming
Evolutionary Computation
Kernel Trees for Support Vector Machines
IEICE - Transactions on Information and Systems
Creation of Specific-to-Problem Kernel Functions for Function Approximation
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Experiments on kernel tree support vector machines for text categorization
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Optimising multiple kernels for SVM by genetic programming
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Proceedings of the ACM International Conference on Image and Video Retrieval
Global optimization of support vector machines using genetic algorithms for bankruptcy prediction
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Genetic programming for kernel-based learning with co-evolving subsets selection
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An evolutionary approach to automatic kernel construction
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Label dependent evolutionary feature weighting for remote sensing data
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Non-parametric Fisher's discriminant analysis with kernels for data classification
Pattern Recognition Letters
International Journal of Multimedia Data Engineering & Management
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The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings