Download Artificial Intelligence and Soft Computing: 12th by Yoichi Hayashi (auth.), Leszek Rutkowski, Marcin PDF

By Yoichi Hayashi (auth.), Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)

The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed lawsuits of the twelfth foreign convention on man made Intelligence and delicate Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised complete papers awarded including one invited paper have been rigorously reviewed and chosen from 274 submissions. The fifty seven papers incorporated within the first quantity are geared up within the following topical sections: neural networks and their functions; fuzzy structures and their purposes; development category; and desktop imaginative and prescient, photograph and speech analysis.

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Extra info for Artificial Intelligence and Soft Computing: 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part I

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4) The interpolation error |f (x) − s(x)| can be bounded by |f (x) − s(x)| ≤ Pϕβ ,X (x)|f |Nϕβ (Ω) , where Pϕβ ,X is a power function defined as N Pϕβ ,X (x)2 := ϕβ (0) − 2 N uj (x)ϕβ (||x − xj ||) + j=1 ui (x)uj (x)ϕβ (||xi − xj ||) i,j=1 (5) and |·|Nϕβ (Ω) is a norm in the native space generated by the radial basis function ϕβ (reproducing kernel Hilbert space with ϕβ as its reproducing kernel) [35]. Notice, that one can calculate a value of the power function (5) for any point of the domain Ω using (4).

In each of these data sets, as in the CARD1 data, the attributes on each line are labeled with a D for a discrete attribute or a C for a continuous attribute. Our experimental results are shown in Table 1, which shows the recognition rates for the data sets, and Table 2, which shows the number of rules. Our E-Re-RX results are presented with reference to Hara and Hayashi [13], [37]. Our Re-RX results are 14 Y. Hayashi presented with reference to Setiono et al. [9]. Our results for MNNEX, PITS, Neural Network Bagging (shown as “NNBagging” in Table 1), and Neural Network AdaBoosting (shown as “NN AdaBoosting” in Table 1) are presented with reference to Akhand et al.

In the classical case the neural networks learning algorithms are implemented on serial computer. Unfortunatelly, this method is slow because the learning algorithm requires high computational load. Therefore, high performance dedicated parallel structure is a suitable solution, see eg. [2] - [5], [13], [14]. This paper presents a new concept of the parallel realisation of the Jordan learning algorithm. A single iteration of the parallel architecture requires much less computation cycles than a serial implementation.

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