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» The Parameter-Less Self-Organizing Map algorithm
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NECO
1998
116views more  NECO 1998»
13 years 9 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...
AROBOTS
2008
131views more  AROBOTS 2008»
13 years 9 months ago
Active audition using the parameter-less self-organising map
This paper presents a novel method for enabling a robot to determine the position of a sound source in three dimensions using just two microphones and interaction with its environm...
Erik Berglund, Joaquin Sitte, Gordon Wyeth
EOR
2006
65views more  EOR 2006»
13 years 9 months ago
Self-organizing maps could improve the classification of Spanish mutual funds
In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. Th...
David Moreno, Paulina Marco, Ignacio Olmeda
JMM2
2008
92views more  JMM2 2008»
13 years 9 months ago
Dimensionality Reduction using SOM based Technique for Face Recognition
Unsupervised or Self-Organized learning algorithms have become very popular for discovery of significant patterns or features in the input data. The three prominent algorithms name...
Dinesh Kumar, C. S. Rai, Shakti Kumar
ICDM
2003
IEEE
101views Data Mining» more  ICDM 2003»
14 years 3 months ago
Interactive Visualization and Navigation in Large Data Collections using the Hyperbolic Space
We propose the combination of two recently introduced methods for the interactive visual data mining of large collections of data. Both, Hyperbolic Multi-Dimensional Scaling (HMDS...
Jörg A. Walter, Jörg Ontrup, Daniel Wess...