A new method for sparsity control in support vector classification and regression
| Autor: | Pierre M. L. Drezet, Robert F. Harrison |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Learning vector quantization
Structured support vector machine business.industry Feature vector Pattern recognition Machine learning computer.software_genre Relevance vector machine Support vector machine ComputingMethodologies_PATTERNRECOGNITION Kernel method Artificial Intelligence Signal Processing Least squares support vector machine Sequential minimal optimization Computer Vision and Pattern Recognition Artificial intelligence business computer Software Mathematics |
| Zdroj: | Pattern Recognition. 34:111-125 |
| ISSN: | 0031-3203 |
| Popis: | A new method of implementing Support Vector learning algorithms for classification and regression is presented which deals with problems of over-defined solutions and excessive complexity. Classification problems are solved with a minimum number of support vectors, irrespective of the degree of overlap in the training data. Support vector regression can deliver a sparse solution, without requiring Vapnik's e-insensitive zone. This paper generalises sparsity control for both support vector classification and regression. The novelty in this work is in the method of achieving a sparse support vector set which forms a minimal basis for the prediction function. |
| Databáze: | OpenAIRE |
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