Name
Title | ||
|---|---|---|
A Hybrid Genetic Programming and Boosting Technique for Learning Kernel Functions from Training Data |
Abstract | ||
|---|---|---|
This paper proposes a technique for learning kernel functions that can be used in non-linear SVM classification. The technique uses genetic programming to evolve kernel functions as additive or multiplicative combinations of linear, polynomial and RBF kernels, while a procedure inspired from InfoBoost helps the evolved kernels concentrate on the most difficult objects to classify. The kernels obtained at each boosting round participate in the training of non-linear SVMs which are combined, along with their confidence coefficients, into a final classifier. We compared on several data sets the performance of the kernels obtained in this manner with the performance of classic RBF kernels and of kernels evolved using a pure GP method, and we concluded that the boosted GP kernels are generally better. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/SYNASC.2007.71 | Timisoara |
Keywords | Field | DocType |
pure gp method,rbf kernel,final classifier,training data,hybrid genetic programming,kernel function,learning kernel functions,classic rbf kernel,confidence coefficient,non-linear svm classification,boosting technique,non-linear svms,difficult object,gp kernel,genetic algorithms,support vector machines,learning artificial intelligence | Graph kernel,Least squares support vector machine,Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Computer science,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning,Kernel (statistics) | Conference |
ISBN | Citations | PageRank |
0-7695-3078-8 | 1 | 0.37 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta Gîrdea | 1 | 129 | 6.84 |
| Liviu Ciortuz | 2 | 24 | 4.84 |