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IJON
2002
84views more  IJON 2002»
13 years 4 months ago
On the generative probability density model in the self-organizing map
The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A major drawback of the SOM has been the lack of a theoretically justified criterion for model se...
Timo Kostiainen, Jouko Lampinen
NECO
1998
116views more  NECO 1998»
13 years 4 months ago
GTM: The Generative Topographic Mapping
Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example ...
Christopher M. Bishop, Markus Svensén, Chri...
BMCBI
2004
166views more  BMCBI 2004»
13 years 4 months ago
Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models
Background: Many current gene prediction methods use only one model to represent proteincoding regions in a genome, and so are less likely to predict the location of genes that ha...
Shaun Mahony, James O. McInerney, Terry J. Smith, ...
ICANN
2010
Springer
13 years 5 months ago
Self-Organization of Steerable Topographic Mappings as Basis for Translation Invariance
One way to handle the perception of images that change in position (or size, orientation or deformation) is to invoke rapidly changing fiber projections to project images into a fi...
Junmei Zhu, Urs Bergmann, Christoph von der Malsbu...
EELC
2006
128views Languages» more  EELC 2006»
13 years 8 months ago
Evolving Distributed Representations for Language with Self-Organizing Maps
We present a neural-competitive learning model of language evolution in which several symbol sequences compete to signify a given propositional meaning. Both symbol sequences and p...
Simon D. Levy, Simon Kirby