Results 231 to 240 of about 25,182 (261)

Self-Organizing Maps for imprecise data

Fuzzy Sets and Systems, 2014
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D'URSO, Pierpaolo, Livia De Giovanni, MASSARI, Riccardo   +2 more
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A Pareto Self-Organizing Map

2002
Self Organizing Features Maps are used for a variety of tasks in visualization and clustering, acting to transform data from a highdimensional original feature space to a (usually) two-dimensional grid. SOFMs use a similarity metric in the input space, and this composes individual feature differences in a way that is not always desirable.
Kennedy, Richard Lee, Hunter, Andrew
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Self-organizing Map Initialization

2005
The solution obtained by Self-Organizing Map (SOM) strongly depends on the initial cluster centers. However, all existing SOM initialization methods do not guarantee to obtain a better minimal solution. Generally, we can group these methods in two classes: random initialization and data analysis based initialization classes.
Attik, Mohammed, Bougrain, Laurent, Alexandre, Frédéric   +2 more
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Probabilistic PCA Self-Organizing Maps

IEEE Transactions on Neural Networks, 2009
In this paper, we present a probabilistic neural model, which extends Kohonen's self-organizing map (SOM) by performing a probabilistic principal component analysis (PPCA) at each neuron. Several SOMs have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model while it has a low ...
Ezequiel López-Rubio, Juan Miguel Ortiz-de-Lazcano-Lobato, Domingo López-Rodríguez   +2 more
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Recursive self-organizing maps

Neural Networks, 2002
This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities.
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Syntactical Self-Organizing Map

1997
In this paper a new neural network structure, called Syntactical Self-Organizing Map (SSOM), is introduced. SSOM is obtained from classical (numerical) Kohonen neural network and is specifically for classifying the syntactical structures, like: strings, trees or graphs.
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A Bayesian Analysis of Self-Organizing Maps

Neural Computation, 1994
In this paper Bayesian methods are used to analyze some of the properties of a special type of Markov chain. The forward transitions through the chain are followed by inverse transitions (using Bayes' theorem) backward through a copy of the same chain; this will be called a folded Markov chain.
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Paths of Wellbeing on Self-Organizing Maps

2013
In this article, we introduce the concept of pathways of wellbeing and examine how such paths can be discovered from large data sets using the self-organizing map. Data sets used in the illustrative experiments include measurements of physical fitness and subjective assessments related to diagnosing work stress.
Krista Lagus, Tommi Vatanen, Oili Kettunen, Antti Heikkilä, Matti Heikkilä, Mika Pantzar, Timo Honkela   +6 more
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Self-organizing maps for internal representations

Psychological Research, 1990
One of the biological mechanisms that has so far been poorly understood is the ability of the brain to form representations of primary sensory experiences at increasingly higher levels of abstraction. At many lower perceptual levels, sensory information first becomes represented in topographically ordered sensory maps.
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Reservoir Self Organizing Map

2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2022
Hiroshi Dozono, Ryuhei Matsuo, Koki Yoshioka   +2 more
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