Soft Computing in Case Based Reasoning by Julie Main, Tharam S. Dillon, Simon C. K. Shiu (auth.),

By Julie Main, Tharam S. Dillon, Simon C. K. Shiu (auth.), Sankar K. Pal MTech, PhD, DIC, Fellow IEEE, Tharam S. Dillon BE, PhD, FSaRS, FIE, SMIEEE, Daniel S. Yeung BA, MA, MSc, MBA, PhD, FHKIE (eds.)

Soft Computing in Case dependent Reasoning demonstrates how a variety of smooth computing instruments should be utilized to layout and advance methodologies and platforms with case established reasoning for real-life decision-making or popularity problems.
Comprising contributions from specialists from around the world, it:
- presents an advent to CBR and gentle computing, and the relevance in their integration
- Evaluates the strengths and weaknesses of CBR in its present form
- offers fresh advancements and demanding purposes in domain names equivalent to data-mining, scientific prognosis, knowledge-based professional structures, banking, and forensic investigation
- Addresses new details on constructing clever systems
This booklet should be of specific curiosity to graduate scholars and researchers in desktop technological know-how, electric engineering and knowledge expertise however it can also be of curiosity to researchers and practitioners within the fields of structures layout, development reputation and knowledge mining.

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Extra resources for Soft Computing in Case Based Reasoning

Example text

B. T. Keane, Adaptation-guided retrieval: questioning the similarity assumption in reasoning. Artificial Intelligence, 102: 249-293, 1998. 12. E. Simoudis, Using case-based retrieval for customer technical support. IEEE Expert, 7(5):7-12, 1992. 13. M. Adalier and C. Tsatsoulis, Redesigning for manufacturability using REINRED. Applied Artificial Intelligence, 6(3) :285-302, 1992. 14. B. C. l97-207. 15. B. Smyth and E. McKenna, Modelling the competence of case-bases, in Proceedings of Fourth European Workshop, EWCBR-98, 1998, pp.

We may then regard the fuzzy subsets Kc ~! U for e E K and K c ~!

Y. = Consequences are I(x, 0) 0 for t-norrns, and I(x, 1) 1 for co-t-norrns, respectively. There are other fuzzy combination rules available which are fuzzy versions of general Boolean operators like different types of implication. 2 Similarities and Fuzzy Sets: First Observations For any similarity measure SIM over U we define a fuzzy subset SIM UxUby JLSIM(U,V):= SIM(u,v). 33) where symmetry of SIM implies JLu(v) = JLv(u). Starting with a fixed SIM, we obtain for each u E U some fuzzy subset Fu , which describes how U is structured by SIM from the viewpoint of u.

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