Published May 29, 2023
| Version v1
Publication
A new Kernel to use with discretized Temporal Series
Contributors
Others:
- Ortega Ramírez, Juan Antonio
- Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
- Universidad de Sevilla. Departamento de Economía Aplicada I
- Junta de Andalucía
- Comisión Interministerial de Ciencia y Tecnología (CICYT). España
- Comisión Interministerial de Ciencia y Tecnología (CICYT). España
Description
In this papera new kernel, from statistical learning theory, is proposed to work with symbols chains (words) obtained from a discretization procedure of a continuous feature. Meanwhile the exact definition of the discretization is not strictly necessary, it must ever exist either, a distance or a similarity measure between symbols in a certain alphabet (a set of symbols). The proposed kernel is a generalization of a dot product in a vector space, not necessarily provided of any mathematical structure, that will allows to establish a similarity measure between objects of the alphabet. This kernel is applied on a set of television shares obtained from the seven main television stations in Andalusia (Spain). A comparative study for classification purposes is done, and the associated parameter selection is studied.
Abstract
Junta de Andalucía ACC-265-TIC-2001Abstract
Junta de Andalucía ACPAl-2003/014Abstract
Comisión Interministerial de Ciencia y Tecnología DP12001-4404-EAbstract
Comisión Interministerial de Ciencia y Tecnología TIC2003-09 l 74-C02- 01Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/146738
- URN
- urn:oai:idus.us.es:11441/146738