Results 231 to 240 of about 9,834 (275)

Alternative learning vector quantization

Pattern Recognition, 2006
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Kuo-Lung Wu, Miin-Shen Yang
exaly   +2 more sources

Soft Learning Vector Quantization

Neural Computation, 2003
Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function
Sambu Seo, Klaus Obermayer
openaire   +3 more sources

Variants of recurrent learning vector quantization

Neurocomputing, 2022
Jensun Ravichandran, Marika Kaden, Thomas Villmann   +2 more
exaly   +2 more sources

Regression Learning Vector Quantization

2009 Ninth IEEE International Conference on Data Mining, 2009
Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. In this paper we propose an extension of the LVQ algorithm to regression.
Mihajlo Grbovic, Slobodan Vucetic
openaire   +1 more source

Competitive learning algorithms for vector quantization

Neural Networks, 1990
Abstract We compare a number of training algorithms for competitive learning networks applied to the problem of vector quantization for data compression. A new competitive-learning algorithm based on the “conscience” learning method is introduced. The performance of competitive learning neural networks and traditional non-neural algorithms for vector
Stanley C Ahalt, Ashok K Krishnamurthy
exaly   +2 more sources

A review of learning vector quantization classifiers [PDF]

Neural Computing and Applications, 2013
14 ...
Pablo A Estévez, Estévez Pablo A
exaly   +5 more sources

Expansive and Competitive Learning for Vector Quantization

Neural Processing Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José Muñoz-Pérez, José Antonio Gómez-Ruiz, Ezequiel López-Rubio, M. Angeles García-Bernal   +3 more
openaire   +1 more source

A dynamic approach to learning vector quantization

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Learning Vector Quantization networks are generally considered a powerful pattern recognition tool. Their main drawback, however, is the Competitive Learning algorithm they are based upon, that suffers of the so called underutilized or dead unit problem.
Claudio De Stefano, Ciro D'Elia, Angelo Marcelli   +2 more
openaire   +1 more source