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 e-book constitutes the refereed complaints of the fifth motels IAPR TC3 GIRPR overseas Workshop on man made Neural Networks in development 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 variety of themes within the box of neural community- and computer learning-based development reputation offering and discussing the most recent examine, effects, and concepts in those areas.

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Extra resources 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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K | {cj ∈ Em | cj (v) = i} | 3 Experiments We applied our data set characterization as well as the ensemble classifier to several well-known microarray data sets: The Bittner data set [14] contains expression profiles of 31 melanomas and 7 controls in 8067 features. The initial analysis of this data showed a stable cluster of 19 of the melanomas. In this analysis, the samples from this cluster (ML1) and the 19 remaining samples (melanomas and controls, ML2) were treated as distinct classes. The Golub data set [15] contains data from a microarray experiment of acute Leukemia.

Its interpretation is not obvious: the first part, , | | the difference between the ground truth and network posterior, is easy to understand; while the second part, ∑ | | | , is more mysterious. It is hard to give a good interpretation of this sum but from our understanding it arises Incremental Learning by Message Passing in Hierarchical Temporal Memory 35 due to the fact that we are dealing with probabilities. None of the parts can be ignored; tests have shown that they are both important to produce good results.

A coincidence, , is a vector representing a prototypical activation pattern of the node’s children. For a node in , with input nodes as children, this corresponds to an image patch of the same size as the node’s receptive field. For nodes higher up in the hierarchy, with intermediate nodes as children, each element of a coincidence, , is the index of a coincidence group in child . Coincidence groups, also called Incremental Learning by Message Passing in Hierarchical Temporal Memory 27 temporal groups, are clusters of coincidences likely to originate from simple variations of the same input pattern.

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