By Leon Bobrowski (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada (eds.)
This ebook constitutes the refereed court cases of the eighth overseas convention on synthetic Intelligence and gentle Computing, ICAISC 2006, held in Zakopane, Poland, in June 2006.
The 128 revised contributed papers offered have been rigorously reviewed and chosen from four hundred submissions. The papers are prepared in topical sections on neural networks and their purposes, fuzzy platforms and their purposes, evolutionary algorithms and their functions, tough units, class and clustering, photograph research and robotics, bioinformatics and clinical purposes, a number of difficulties of man-made intelligence.
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Extra info for Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings
In the ﬁrst phase, the experiment shows the results of the comparing error scales between classiﬁcation and regression problems. The second experiment is conducted under artiﬁcially composed ill-posed problem by Gaussian distribution. Finally this paper proves that the suggested dynamic momentum ﬁnds the regularization parameter more precisely than others. 2 Modiﬁed SVM The original SVM[3,4,7,10,11] is deﬁned as followed by N L(α) = b αi − i=1 N subject to (1) 1 2 N N i=1 j=1 αi αj di dj xTi xj (1) αi di = 0 (2) 0 ≤ αi ≤ C i=1 Where b is margin, is weighting vector, d is the destination of training data, which is used to expressed with positive class and negative class, and x is input vector.
24 M. Gorgo´ n and M. Wrzesi´ nski Because the set of intervals covers the whole h domain in a continuous way, therefore the i-th segment of the set covers the interval hi , hi+1 ). The realized approximation of the activation function can be formally written as: Δh = h − hi O(h) = Oi + ai Δh , (2) where i satisﬁes the following inequality: hi ≤ h < hi+1 . (3) The relations (2) and (3) deﬁne the calculation tasks of the activation block. e. the solution of inequality (3) with respect to i. The second tasks comprises the reading data from the record describing the required i-th segment and executing the calculations according to (2).
KLMSBLDM(testing) Fig. 6. 4 shows KLMSBL which does not apply dynamic momentum. 3, although the values of both RMSE, which does not include a bias term, and RMSEB, which includes a bias term within learning weight vectors, are similar, RMS errors in the method using matrix inverse with regularization parameter are mostly lower than those of other methods. 4, RMS errors in RMSE and RMSEB are similar each other, but compared with these values, InvRMSE and InvRMSEB have large values. These results show that the learning methods which do not include DM found local optimum value upon typical patterns or overly ﬁtted in training data, but KLMSBLDM found globally optimized value.
Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings by Leon Bobrowski (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada (eds.)