Download e-book for kindle: Artificial Neural Networks in Pattern Recognition: Second by Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai

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

ISBN-10: 3540379517

ISBN-13: 9783540379515

This publication constitutes the refereed court cases of the second one IAPR Workshop on synthetic 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, aid vector studying, a number of classifier platforms, visible item reputation, and knowledge mining in bioinformatics.

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

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Accuracy on the training and test set achieved by supervised batch NG (BSNG) and batch NG (BNG) on the Wisconsin breast cancer dataset for different mixing parameters α reported. 2. e. more emphasis of the given data labels. Thereby α must not become 0 which corresponds to a pure label adaptation without adaptation of the prototypes. α = 1 corresponds to standard NG. Obviously, the classification accuracy of simple NG is inferior compared to the supervised version due to the overlap of classes 2 and 3 which is not accounted for by the overall statistics of the input vectors.

Pre-synaptic lateral inhibition provides a better arcitecture for self-organizing neural networks. Network: Computation in Neural Systems 10, 285–301. Spratling, M. , Johnson, M. H. (2002). Pre-integration lateral inhibition enhances unsupervised learning. Neural Computation 14 (9), 2157–2179. , Nienhuis, B. (1993). Pattern recognition in the neocognitron is improved by neuronal adaptation. Biological Cybernetics 70, 47–53. Supervised Batch Neural Gas Barbara Hammer1 , Alexander Hasenfuss1 , Frank-Michael Schleif2 , and Thomas Villmann3 1 Clausthal University of Technology, Institute of Computer Science, Clausthal-Zellerfeld, Germany 2 University of Leipzig, Institute of Computer Science, Germany 3 University of Leipzig, Clinic for Psychotherapy, Leipzig, Germany Abstract.

Strickert, and T. Villmann (2005), Supervised neural gas with general similarity measure, Neural Processing Letters 21(1), 21-44. 7. B. Hammer, and T. Villmann (2002), Generalized relevance learning vector quantization, Neural Networks 15, 1059-1068. 8. 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.

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Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings by Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai (eds.)


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