An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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)
Lagrangian support vector machines
The Journal of Machine Learning Research
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
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
A model for a complex polynomial SVM kernel
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
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Kernel-based learning presents a unified approach to machine learning problems such as classification and regression. The selection of a kernel and associated parameters is a critical step in the application of any kernel-based method to a problem. This paper presents a data-driven evolutionary approach for constructing kernels, named KTree. An application of KTree to the Support Vector Machine (SVM) classifier is described. Experiments on a synthetic dataset are used to determine the best evolutionary strategy, e.g. what fitness function to use for kernel evaluation. The performance of an SVM based on KTree is compared with that of standard kernel SVMs on a synthetic dataset and on a number of real-world datasets. KTree is shown to outperform or match the best performance of all the standard kernels tested.