By A. Klose, A. Nürnberger, D. Nauck, R. Kruse (auth.), Dr. Abraham Kandel, Dr. Mark Last, Dr. Horst Bunke (eds.)

The quantity bargains a complete insurance of the hot advances within the program of soppy computing and fuzzy common sense idea to information mining and data discovery databases. It makes a speciality of a number of the toughest, and but unsolved, problems with information mining like understandability of styles, discovering complicated relationships among attributes, dealing with lacking and noisy information, mining very huge datasets, swap detection in time sequence, and integration of the invention approach with database administration systems.

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1999). NEFCLASS for JAVA - New Learning Algorithms, In: Proc. 18th International Conf. of the North American Fuzzy Information Processing Society (NAFIPS'99), pp. 472-476, IEEE, New York, NY. 34. NUrnberger, A, Klose, A, and Kruse, R (2000). Analyzing Borders Between Partially Contradicting Fuzzy Rules. In Proc. of the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS'2000), NAFIPS, Atlanta. 35. NUrnberger, A, Klose, A, and Kruse, R (1999). Discussing Cluster Shapes of Fuzzy Classifiers.

Multi-Layer Perceptron (MLP): A state of the art neural network with 36 inputs, 40 hidden units, and pruned to an optimized model with 12 inputs and 27 weights 45. The performance of the strategies are compared by computing the profit and loss curves (P&L) on the test set. The P&L curve measures the return of a trading system by adding up the gains from the transactions. To measure the accuracy of the predictions, the mean squared error (MSE) is given. The optimized neuro-fuzzy model consists of 31 rules and 22 membership functions.

M-a) forxE[a,m] 2 (m -a) . For x = m we obtain 44 The Gaussian membership function is governed by the expression A(x) = exp( - (x - m)2/ ( 2) and includes two parameters (m and 0). The first one (m) determines the position of the fuzzy set. The second parameter (0) controls the spread of the information granule. Gaussian fuzzy sets are symmetrical. This may be a problem when the data Figure a significant asymmetry that cannot be easily coped with. 5, see Figure 3. I.. ... I. 2 I.. (a) (b) Fig.

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