Titlebook: Advances in Self-Organizing Maps; 8th International Wo Jorma Laaksonen,Timo Honkela Conference proceedings 2011 Springer-Verlag GmbH Berlin

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Aerate

Pekka J. Korhonen,Aapo Siljam?kirameters of a local affine transformation associated with each neuron are updated by an evolutionary algorithm and used to map each template’s keypoint in the previous frame to the current one. Computer simulations indicate that the proposed approach presents better results than those obtained by a direct method approach.

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Mahmoud Seyedsadr,Paul L. Corneliusihood function to the pairwise-based measures. Our method can extend any clustering measure based on set or pairwise of data points. The present paper examined the topographic component of the extended measure and revealed an appropriate neighborhood radius of the topographic measures.

Adherent

CROW

Fiona K. Hamey,Berthold G?ttgensl that users have the possibility of interactively investigating the data set. This work provides an overview of state-of-the-art software tools for SOM-based visual data exploration. We discuss the functionality of software for specialized data sets, as well as for arbitrary data sets with a focus on interactive data exploration.

Melodrama

Sparse Functional Relevance Learning in Generalized Learning Vector Quantization set of basis functions depending on only a few parameters compared to standard relevance learning. Moreover, the sparsity of the superposition is achieved by an entropy based penalty function forcing sparsity.

清真寺

Relevance Learning in Unsupervised Vector Quantization Based on Divergencesen vector quantization cost function. We consider several widely used models including the neural gas algorithm, the Heskes variant of self-organizing maps and the fuzzy c-means. We apply the relevance learning scheme for divergence based similarity measures between prototypes and data vectors in the vector quantization schemes.

Libido

https://doi.org/10.1007/978-3-642-21566-7ANN; SOM algorithms; bioinspired computing; computational intelligence; natural computing; neural network

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