By Botond Attila Bócsi, Lehel Csató (auth.), Valeri Mladenov, Petia Koprinkova-Hristova, Günther Palm, Alessandro E. P. Villa, Bruno Appollini, Nikola Kasabov (eds.)
The booklet constitutes the court cases of the twenty third overseas convention on man made Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The seventy eight papers incorporated within the complaints have been rigorously reviewed and chosen from 128 submissions. the focal point of the papers is on following issues: neurofinance graphical community types, mind laptop interfaces, evolutionary neural networks, neurodynamics, advanced structures, neuroinformatics, neuroengineering, hybrid platforms, computational biology, neural undefined, bioinspired embedded structures, and collective intelligence.
Read Online or Download Artificial Neural Networks and Machine Learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings PDF
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Extra info for Artificial Neural Networks and Machine Learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings
2. 1 Algorithms in Competition The following algorithms conﬁgurations are compared: – EM-ML: the proposed maximum likelihood approach via the EM algorithm where the variances vk2 are automatically estimated, – EM-MAP(vk2 ): the EM algorithm of Calabrese and Paninski  based on maximum a posteriori approach. 01}, – EM-ML(vk2 ): the proposed algorithm run with ﬁxed values of the variances vk2 ∈ S. It should be noted here that this version of the EM-ML algorithm, operating with ﬁxed values of vk2 , is only used to observe the performances of our approach when it is placed in the same conditions as the EM-MAP algorithm, – theoretical: the EM-ML algorithm run using the true initial parameters.
IB2 Algorithm. IB2 belongs to the well-known family of IBL algorithms [2,1]. It is based on CNN-rule. Actually, IB2 is a simple one pass variation of CNNrule. Each TS item x is classiﬁed using 1NN on the current CS. If x is classiﬁed correctly, it is discarded. Otherwise, x is transferred to CS. Contrary to CNNrule, IB2 does not ensure that all discarded items can be correctly classiﬁed by the ﬁnal content of CS. However, since it is a one-pass algorithm, it is very fast. In addition, IB2 builds its CS incrementally.
2(b) shows the sample size of each subset for Tree-LSH-GPR and NaiveLSH-GPR. The red bar and blue bar show the divided result of Naive-LSHGPR and Tree-LSH-GPR. Tree-LSH-GPR can divide the dataset into equal-size subsets. On the other hand, the sample sizes obtained by Naive-LSH-GPR are quite diﬀerent. Table 1 shows the computational time for prediction and inverse matrix. Both Naive-LSH-GPR and Tree-LSH-GPR can calculate faster than the Full-GPR. Although the computational time by Naive-LSH-GPR with L = 1 was reduced to 1/10 for prediction and 1/80 for inverse matrix compared to Full-GPR, it has large variance.
Artificial Neural Networks and Machine Learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings by Botond Attila Bócsi, Lehel Csató (auth.), Valeri Mladenov, Petia Koprinkova-Hristova, Günther Palm, Alessandro E. P. Villa, Bruno Appollini, Nikola Kasabov (eds.)