By Botond Attila Bócsi, Lehel Csató (auth.), Valeri Mladenov, Petia Koprinkova-Hristova, Günther Palm, Alessandro E. P. Villa, Bruno Appollini, Nikola Kasabov (eds.)
The publication constitutes the court cases of the twenty third foreign convention on man made Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The seventy eight papers incorporated within the lawsuits have been conscientiously reviewed and chosen from 128 submissions. the focal point of the papers is on following issues: neurofinance graphical community versions, mind desktop interfaces, evolutionary neural networks, neurodynamics, complicated platforms, neuroinformatics, neuroengineering, hybrid structures, computational biology, neural undefined, bioinspired embedded platforms, and collective intelligence.
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Additional info for Artificial Neural Networks and Machine Learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings
Corr. 2nd printing edition. Springer (October 2006) 2. : Greedy block coordinate descent for large scale gaussian process regression. Computing Research Repository (2012) 3. : Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262 (2004) 4. : Stable and eﬃcient gaussian process calculations. Journal of Machine Learning Research 10, 857–882 (2009) 5. : Training products of experts by minimizing contrastive divergence.
Fig. 3(b) shows the normalized mean squared error (nMSE) of the regression result and calculation time. The blue solid line, green solid line, red solid line and black solid line in Fig. 3(b) show the regression results of Tree-LSH-GPR-3-L, Tree-LSH-GPR-4-L, Tree-LSH-GPR-5-L and FULL-GPR, respectively. nMSE is large when the number of hash-keys is small. However, the performance of Tree-LSH-GPR gets close to the Full-GPR by increasing the number of hash-keys. Error caused by dataset division can be reduced by increasing the number of hash-keys.
The median correlation coeﬃcient 32 J. Schumacher, H. Toutounji, and G. Pipa Fig. 2. 2. 32 and p = 1 (5-bit parity), respectively. Results are reported for 400 neurons (τ = 80) on data sets of size 3000/1000 (training/validation) in ﬁgures 2A and 2B, size 3000/1000 in 2C (right plot), as well as for data sets of size 10000/10000 in ﬁgure 2C (left plot). Each plot is generated from 50 diﬀerent trials. The plots show median (black horizontal bar), 25th /75th percentiles (boxes), and most extreme data points not considered outliers (whiskers).