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Automating the construction of CBR systems using kernel methods
Author(s) -
Fyfe Colin,
Corchado Juan M.
Publication year - 2001
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.1024
Subject(s) - computer science , artificial intelligence , kernel (algebra) , case based reasoning , reuse , context (archaeology) , automation , reasoning system , machine learning , kernel method , model based reasoning , support vector machine , data mining , knowledge representation and reasoning , mathematics , mechanical engineering , ecology , paleontology , combinatorics , engineering , biology
Instance‐based reasoning systems and, in general, case‐based reasoning systems are normally used in problems for which it is difficult to define rules. Although case‐based reasoning methods have proved their ability to solve different types of problems, there is still a demand for methods that facilitate their automation during their creation and the retrieval and reuse stages of their reasoning circle. This paper presents one method based on kernels, which can be used to automate some of the reasoning steps of instance‐based reasoning systems. 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). Unsupervised kernel methods have been used successfully to identify the optimal instances to instantiate an instance base. The efficiency of the kernel model is shown on an oceanographic problem. © 2001 John Wiley & Sons, Inc.