Artificial Neural Networks in Pattern Recognition: 5th INNS by Bassam Mokbel, Sebastian Gross, Markus Lux, Niels Pinkwart,

By Bassam Mokbel, Sebastian Gross, Markus Lux, Niels Pinkwart, Barbara Hammer (auth.), Nadia Mana, Friedhelm Schwenker, Edmondo Trentin (eds.)

This booklet constitutes the refereed court cases of the fifth lodges IAPR TC3 GIRPR foreign Workshop on synthetic Neural Networks in trend reputation, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 revised complete papers awarded have been rigorously reviewed and chosen for inclusion during this quantity. They conceal a wide range of issues within the box of neural community- and laptop learning-based development acceptance providing and discussing the most recent learn, effects, and ideas in those areas.

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Additional info for Artificial Neural Networks in Pattern Recognition: 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings

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14) (eq. 16) ) } By updating the probability matrices for every training example, instead of at the end of the presentation of a group of patterns, an online version of the algorithm is obtained. Both batch and online versions of HSR are investigated in the experimental section. In many cases it is preferable for the nodes in lower intermediate levels to share memory, so called node sharing [7]. This speeds up training and forces all the nodes of the level to respond in the same way when the same stimulus is presented at Incremental Learning by Message Passing in Hierarchical Temporal Memory 31 different places in the receptive field.

In the experiments presented in this paper two iterations per epoch were used, and the optimal learning rate was found therefor. With more iterations a lower learning rate would likely be optimal. The difference in suitable learning rate between the intermediate and the output level is also an important finding and can probably be explained by the fact that the matrix of the output node has a much more direct influence on the network posterior. The output node memory is also trained supervised in the pre-training while the intermediate nodes are trained unsupervised, which might suggest that there is more room for fine tuning in the intermediate nodes.

Topographic mapping of large dissimilarity datasets. Neural Computation 22(9), 2229–2284 (2010) 10. : Universal approximation capability of cascade correlation for structures. Neural Computation 17, 1109–1159 (2005) 11. : Generalized relevance learning vector quantization. Neural Networks 15(8-9), 1059–1068 (2002) 12. : Self-Oganizing Maps, 3rd edn. Springer (2000) 13. : How to make large self-organizing maps for nonvectorial data. Neural Networks 15(8-9), 945–952 (2002) 14. : The Dissimilarity Representation for Pattern Recognition.

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