Towards a modeling framework for integrating hybrid techniques

Author

Gómez-Skarmeta, Antonio F. ; Jiménez, Fernando ; Valdés, Mercedes ; Botía, Juan A. ; Padilla, Antonio M.

Author_Institution

Departamento de Ingenieria de la Informacion y las Comunicaciones, Murcia Univ., Spain

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

985

Lastpage

990

Abstract

Nowadays, it is easy to find a number of different hybrid approaches for fuzzy modeling. All these approaches were built in a very ad-hoc manner, and did not follow a systematic approach. However, we think that some kind of information system which helps in the study of how algorithms can combine to model systems in a fuzzy fashion should be very helpful. In this article, we propose METALA (META-Learning Architecture), an architecture to study the typical processes of machine learning, to study the particular issue of fuzzy modeling

Keywords

fuzzy logic; learning (artificial intelligence); software architecture; METALA; fuzzy modeling; hybrid techniques integration; machine learning; meta-learning architecture; modeling framework; Context modeling; Fuzzy sets; Fuzzy systems; Information systems; Input variables; Learning systems; Machine learning; Machine learning algorithms; Merging; Parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1006638

Filename

1006638