Read e-book online Artificial Neural Networks in Pattern Recognition: Second PDF

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 provided have been rigorously 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 structures, visible item attractiveness, and knowledge mining in bioinformatics.

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Read Online or Download Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings PDF

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We find ˆ E(W , Λ , Y ) = Q(W , Λ , Y , W , Λ , Y ) ≤ Q(W, Λ, Y, W , Λ , Y ) because kij (W , Λ , Y ) are optimum assignments given W , Y , λ . Further, ˆ E(W, Λ, Y ) = Q(W, Λ, Y, W, Λ, Y ) ≥ Q(W, Λ, Y, W , Λ , Y ) since W , Y , λ are optimum assignments for given kij . e. the cost function does not increase in batch optimization. Since the assignments kij are unique and they stem from a finite set, the algorithm must converge in a finite number of steps. This shows the convergence of the algorithm in a finite number of optimization steps for all optimization schemes of this form, in particular supervised batch NG.

The data point vi which minimizes the considered sum is taken as wj . This principle has been introduced in [11] for SOM and, including a proof of convergence, in [3] for NG. The transfer to supervised NG or SOM is immediate, whereby optimization can take place either by extensive search, or incorporating (exact or approximate) acceleration as discussed in [2]. References 1. L. Bottou and Y. Bengio (1995), Convergence properties of the k-means algorithm, in NIPS 1994, 585-592, G. S. K. ), MIT. 2.

Thereby, the class information of the data may be fuzzy. The resulting map allows a visualization of the classification process by Corresponding author. F. Schwenker and S. ): ANNPR 2006, LNAI 4087, pp. 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.

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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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