By Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)
The two-volume set LNCS 7367 and 7368 constitutes the refereed lawsuits of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been rigorously reviewed and chosen from a number of submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; photo processing; info processing; neurocontrol; and novel applications.
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Extra resources for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I
The comparison among different threshold and method is shown Fig. 5, and the statistical results are shown in Table 1. Fig. 3. Performance of kernel ELM based on full spectrum Fig. 4. 3500 Modeling Spectral Data Based on MI and Kernel Extreme Learning Machines 35 Fig. 5. Prediction results of the level of saccharose of an orange juice model Table 1. 3775 The prediction results show that the MI-KELM algorithm has the fastest learning speed compare with the normal KELM approaches. With only half of the origin input features, the prediction accuracy is almost same with KELM model based on the full spectrum with lower complexity.
01. Thus, almost all organisms are completely described by common factors. A. Frolov, D. Y. Polyakov Discussion Since LANNIA occurs to be perfect in BFA even with the very noisy artiﬁcial data, it was a challenge for us to apply the method to a large set of natural data. One of the important problems in modern biology is to identify functions of proteins in the organisms. When two proteins show correlated patterns of their presence or absence over the organisms, it is assumed that these proteins are also functionally correlated.
LANNIA revealed 38 factors for four full cycles. Each cycle began by running twenty random trajectories in ANNIA. The Lyapunov functions along the eleven true trajectories at the ﬁrst cycle of LANNIA are shown in Fig. 3(a). At the ﬁrst cycle, the LM procedure converged for ﬁve steps and excluded two factors of eleven. The information gain provided by LM at each its step is shown in Fig. 3(b). 27. At the second full LANNIA cycle, ANNIA found fourteen factors. 31. At the third and fourth full LANNIA cycles, ANNIA found thirteen and twelve factors, LM excluded six and three.