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Erschienen in:
1998 | ReviewPaper | Buchkapitel
Machine learning usefulness relies on accuracy and self-maintenance
verfasst von : Oscar Luaces, Jaime Alonso, Enrique A. de la Cal, José Ranilla, Antonio Bahamonde
Erschienen in: Tasks and Methods in Applied Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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A new machine learning system, INNER, is presented in this paper. The system starts out from a collection of training examples; some of them are inflated generalizing their description so as to obtain a first draft of classification rules. An optimization stage, borrowed from our previous system, Fan, is then applied to return the final set of rules. The main goal of Inner, besides its high level of accuracy, is its ability for self-maintenance. To close the paper, we present a number of different experiments carried` out with INNER to illustrate how good the performance and stability of the system is.