Artificial Neural Networks in Pattern Recognition: Second by Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai

By Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai (eds.)

This e-book constitutes the refereed complaints of the second one IAPR Workshop on man made Neural Networks in trend acceptance, ANNPR 2006, held in Ulm, Germany in August/September 2006.

The 26 revised papers awarded have been conscientiously reviewed and chosen from forty nine submissions. The papers are prepared in topical sections on unsupervised studying, semi-supervised studying, supervised studying, help vector studying, a number of classifier platforms, visible item acceptance, and knowledge mining in bioinformatics.

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Additional resources for Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings

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46–56, 2006. c Springer-Verlag Berlin Heidelberg 2006 FLSOM with Label-Adjusted Prototypes 47 means of the properties of topology preserving mapping of SOMs, which leads to a better understanding of the classification scheme. Further, metric adaptation, as known from learning vector quantization [4],[3], can be easily incorporated into this approach to improve its flexibility. 2 The Self-Organizing Map As mentioned above, SOMs can be taken as unsupervised learning of topographic vector quantization with a topological structure (grid) within the set of prototypes (codebook vectors).

T. Heskes (2001), Self-organizing maps, vector quantization, and mixture modeling, IEEE Transactions on Neural Networks, 12:1299-1305. 9. S. Kaski and J. Sinkkonen (2004), Principle of learning metrics for data analysis, Journal of VLSI Signal Processing, special issue on Machine Learning for Signal Processing, 37: 177-188. 10. T. Kohonen (1995), Self-Organizing Maps, Springer. 11. T. Kohonen and P. Somervuo (2002), How to make large self-organizing maps for nonvectorial data, Neural Networks 15:945-952.

For overlap up to 75% the network quickly learned a selective representation. For higher overlap it took longer training time to reach a selective state. For overlap higher than 88% the network stayed in an unselective state. 008. Because the feedback inhibition reduces the spiking activity in U0 , we compensated this effect by increasing excitatory input strength I0 (see equation 16) when turning on the feedback inhibition. To make sure that the differences in learning speed and learning performance were not caused by these parameter changes, we systematically tested the effect of different input strengths.

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