in-depth analysis of SVM kernel learning and its components
The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learn- ing of good-...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1156 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/1156 |
| Access Level: | acceso abierto |
| Palabra clave: | kernel learning analysis svm genetic programming Automatic machine learning |
| Sumario: | The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learn- ing of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an in- tricate way and it is difficult to provide a comprehensible analysis of their joint effect. In this paper we try to fill this gap introducing the necessary steps in order to understand these interactions and provide clues for the research community to know where to place the emphasis. First of all, we identify all the factors that affect the final performance of support vector machines in relation to the elicitation of kernels. Next, we analyze the factors independently or in pairs and study the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support vector machines. |
|---|