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Those are the names (or identifiers) of the attributes. To add an attribute to an element, you equate a value to the attribute name like this:

6:2:77 6:2:78 6:2:79 6:2:80

Using some of the options just described, the following command line results in the most usable local copy of a web site:

TABLE 14.1 Rule Block Block 0 Block 1 Block 2 Block 3 Rule Block Functions in Pavlov1.par and Pavlov2.par Pavlov1.par stimulus input Quit or put time into new stimulus record associate new stimulus with wired-in response if appropriate reports response, forgets old data, holds generated stimulus-response rules stimulus with known response paired with previous neutral stimulus, moves NewStimulus into short-term memory if latest stimulus paired with more than one neutral stimulus, select latest one counts recent stimulus pairs prevents making new rule if one already exists generates new rule if ready sequences rule blocks Pavlov2.par stimulus input Quit or put time into new stimulus record associate new stimulus with wired-in response if appropriate reports response, forgets old data

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Theorem 7 (Kaklamanis et al. [30]) The WA problem on Tn can be solved using at most 5L/3 colors. The algorithm in work of Kaklamanis et al. [30] is a (5/3)-approximation algorithm for the WA problem on Tn . For the WA problem on Tn , there are instances requiring at least 5L/4 colors [33]. An example of such instances is shown in Figure 18.6 b. In the gure, each set has L/2 paths. The load on T is L and there are 5L/2 paths. It is easy to check that at most two paths can be given the same color. From this, at least (5L/2)/2 = 5L/4 colors are needed. 18.4.5 RWA on Tree of Rings

Kandel A, ed (1991) Fuzzy Expert Systems CRC Press, Boca Raton, Florida Kasabov NK (1998) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering MIT Press, Cambridge, Massachusetts Kecman V (2001) Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Systems (Complex Adaptive Systems) MIT Press, Cambridge, Massachusetts Klir GJ, Yuan B (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications Prentice-Hall PTR, Upper Saddle River, New Jersey Klir GJ, Yuan B, eds (1996) Fuzzy Sets, Fuzzy Logic and Fuzzy Systems: Selected Papers by Lot A Zadeh World Scienti c, Singapore Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controller International Journal of Man-Machine Studies 8(6): 669 678 McDermott J (1980) R1: an expert in the computing system domain In Proceedings of the National Conference on Arti cial Intelligence, 269 271 Minsky M (1975) A framework for representing knowledge.

In The Psychology of Computer Vision, Winston PH, ed, McGraw-Hill, New York Mitchell TM (1997) Machine Learning McGraw-Hill, New York Newell A, Simon HA (1972) Human Problem Solving Prentice-Hall, Englewood Cliffs, New Jersey Pedrycz W (1995) Fuzzy Sets Engineering CRC Press, Boca Raton, Florida Quillian MR (1968) Semantic Memory In Semantic Information Processing, Minsky M, ed, 227 270 MIT Press, Cambridge, Massachusetts Ruspini EH (1982) Possibility approaches for advanced information systems Computer 15: 83 91 Schank RC, Childers PG (1984) The Cognitive Computer: on Language, Learning and Arti cial Intelligence Pearson Addison-Wesley, New York Scott AC, Clayton JE, Gibson EL (1991) A Practical Guide to Knowledge Acquisition Addison-Wesley, New York Shortliffe EH (1976) Computer-Based Medical Consultations: MYCIN Elsevier, New York Siler W, Tucker D, Buckley J (1987).

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A parallel rule ring production system with resolution of memory con icts by weak fuzzy monotonicity, applied to the classi cation of multiple objects characterized by multiple uncertain features International Journal of Man-Machine Studies 26: 321 332 Thomas SF (1995) Fuzziness and Probability ACG Press, Wichita, Kansas Weinschenk JJ, Marks II RJ, Combs WE (2003) Layered URC fuzzy systems: a novel link between fuzzy systems and neural networks Proceedings of the IEEE International Joint Conference on Neural Networks, Portland, Oreson: 2995 3000 Zadeh LA (1965) Fuzzy sets Information and Control 8: 338 353 Zadeh LA (1972) A fuzzy-set-theoretic interpretation of linguistic hedges Journal of Cybernetics 2: 4 34 Zadeh LA (1974) On the analysis of large-scale systems In Fuzzy Sets and their Application to Cognitive and Decision Processes , Academic Press, New York..

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