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