2024-03-28T23:44:43Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1344682024-03-13T09:53:01Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
Gestión del Repositorio Documental de la Universidad de Salamanca
author
Colin, Fyfe
author
Corchado RodrÃguez, Juan Manuel
2017-09-05T11:02:29Z
2017-09-05T11:02:29Z
2002
Advanced Engineering Informatics. Volumen 16 (3), pp. 165-178. Elsevier BV.
1474-0346 (Print)
http://hdl.handle.net/10366/134468
Instance based reasoning systems and in general case based reasoning systems are normally used in problems for which it is difficult to define rules. Instance based reasoning is the term which tends to be applied to systems where there are a great amount of data (often of a numerical nature). The volume of data in such systems leads to difficulties with respect to case retrieval and matching. This paper presents a comparative study of a group of methods based on Kernels, which attempt to identify only the most significant cases with which to instantiate a case base. Kernels were originally derived in the context of Support Vector Machines which identify the smallest number of data points necessary to solve a particular problem (e.g. regression or classification). We use unsupervised Kernel methods to identify the optimal cases to instantiate a case base. The efficiencies of the Kernel models measured as Mean Absolute Percentage Error are compared on an oceanographic problem.
en
Attribution-NonCommercial-NoDerivs 3.0 Unported
Computer Science
A comparison of Kernel methods for instantiating case based reasoning systems
info:eu-repo/semantics/article
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
URL
https://gredos.usal.es/bitstream/10366/134468/1/2002_rev_aei_003.pdf
File
MD5
8484a78fec8a246b9f6213a2532d7661
515800
application/pdf
2002_rev_aei_003.pdf
URL
https://gredos.usal.es/bitstream/10366/134468/4/2002_rev_aei_003.pdf.txt
File
MD5
a3075c45f8182f49f31fe547e5550810
57602
text/plain
2002_rev_aei_003.pdf.txt