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A dynamic adaptive framework for improving case-based reasoning system performance

Type of activity
Theses
Defense's date
2016-02-05
URL
http://hdl.handle.net/2117/96267 Open in new window
Abstract
An optimal performance of a Case-Based Reasoning (CBR) system means, the CBR system must be efficient both in time and in size, and must be optimally competent. The efficiency in time is closely related to an efficient and optimal retrieval process over the Case Base of the CBR system. Efficiency in size means that the Case Library (CL) size should be minimal. Therefore, the efficiency in size is closely related to optimal case learning policies, optimal meta-case learning policies, optimal case...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group
Citation
Orduña, F. "A dynamic adaptive framework for improving case-based reasoning system performance". Tesi doctoral, UPC, Departament de Llenguatges i Sistemes Informàtics, 2016.

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