Titlebook: Advances in Self-Organizing Maps and Learning Vector Quantization; Proceedings of the 1 Thomas Villmann,Frank-Michael Schleif,Mandy Lange C

CRP743

Stuart A. Macgregor,Odile Eisensteinr, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comp

nocturia

obnoxious

內(nèi)閣

https://doi.org/10.1007/978-3-642-18012-5ectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in

大量殺死

limber

Mathew Schwartz,Michael Ehrlicharticularly intuitive framework, in which to discuss the basic ideas of distance based classification. A key issue is that of chosing an appropriate distance or similarity measure for the task at hand. Different classes of distance measures, which can be incorporated into the LVQ framework, are intr

DENT

User Defined Conceptual Modeling Gestures,tion ability with an intuitive learning paradigm: models are represented by few characteristic prototypes, the latter often being located at class typical positions in the data space. In this article we investigate inhowfar these expectations are actually met by modern LVQ schemes such as robust sof

專心

易于出錯

Erdogan Kiran,Ke Liu,Zeynep Bayraktara correction model which is more accurate than the usual one, since we apply different linear models in each cluster of context. We do not assume any particular probability distribution of the data and the detection method is based on the distance of new data to the Kohonen map learned with correcte

地名表

https://doi.org/10.1007/978-1-4615-4371-8s, a Self-Organizing Map (SOM) will be computed using a set of features where each feature is weighted by a relevance factor (RFSOM). These factors are computed using the generalized matrix learning vector quantization (GMLVQ) and allow to scale the input dimensions according to their relevance. Wit