Width optimization of RBF kernels for binary classification of support vector machines: A density estimation-based approach
| Autor: | Antônio de Pádua Braga, Murilo V. F. Menezes, Luiz C. B. Torres |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
Computer science 02 engineering and technology Function (mathematics) Density estimation computer.software_genre 01 natural sciences Support vector machine Kernel (linear algebra) Binary classification Artificial Intelligence Kernel (statistics) 0103 physical sciences Signal Processing Radial basis function kernel 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Data mining 010306 general physics computer Software |
| Zdroj: | Pattern Recognition Letters. 128:1-7 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2019.08.001 |
| Popis: | Kernels are often used for modelling non-linear data, developing a main role in models like the SVM. The optimization of its parameters to better fit each dataset is a frequently faced challenge: A bad choice of kernel parameters often implies a poor model. This problem is usually worked out using exhaustive search approaches, such as cross-validation. These methods, however, do not take into account existent information on data arrangement. This paper proposes an alternative approach, based on density estimation. By making use of density estimation methods to analyze the dataset structure, it is proposed a function over the kernel parameters. This function can be used to choose the parameters that best suit the data. |
| Databáze: | OpenAIRE |
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