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 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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Additional info for Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings
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  for SOM and, including a proof of convergence, in  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 . 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 ,, can be easily incorporated into this approach to improve its ﬂexibility.
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.)