Results 281 to 290 of about 61,624 (324)

CONSTRAINED LEARNING VECTOR QUANTIZATION

International Journal of Neural Systems, 1994
Kohonen’s learning vector quantization (LVQ) is an efficient neural network based technique for pattern recognition. The performance of the method depends on proper selection of the learning parameters. Over-training may cause a degradation in recognition rate of the final classifier. In this paper we introduce constrained learning vector quantization
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Median learning vector quantizer

SPIE Proceedings, 1994
In this paper we propose a novel class of learning vector quantizers (LVQ) based on multivariate data ordering. Linear LVQ is not the optimal estimator for non-Gaussian multivariate data distributions. Furthermore, it is not robust either in the case of outliers or in the case of erroneous decisions. The novel LVQs use multivariate ordering in order to
Ioannis Pitas, P. Kiniklis
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Learning Vector Quantization Networks

Substance Use & Misuse, 1998
(1998). Learning Vector Quantization Networks. Substance Use & Misuse: Vol. 33, No. 2, pp. 271-282.
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Fuzzy learning vector quantization

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005
In this paper, a new supervised competitive learning network model called fuzzy learning vector quantization (FLVQ) which incorporates fuzzy concepts into the learning vector quantization (LVQ) networks is proposed. Unlike the original algorithm, the FLVQ's learning algorithm is derived from optimizing an appropriate fuzzy objective function which ...
null Fu-Lai Chung, null Tong Lee
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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
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Alternative learning vector quantization

Pattern Recognition, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Kuo-Lung, Yang, Miin-Shen
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Recurrent Learning Vector Quantization

2021
Learning Vector Quantization (LVQ) methods have been popular choices of classification models ever since its introduction by T. Kohonen in the 90s. These days, LVQ is combined with Deep Learning methods to provide powerful yet interpretable machine-learning solutions to some of the most challenging computational problems.
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Fuzzy algorithms for learning vector quantization

IEEE Transactions on Neural Networks, 1996
This paper presents the development of fuzzy algorithms for learning vector quantization (FALVQ). These algorithms are derived by minimizing the weighted sum of the squared Euclidean distances between an input vector, which represents a feature vector, and the weight vectors of a competitive learning vector quantization (LVQ) network, which represent ...
N B, Karayiannis, P I, Pai
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Learning vector quantization: The dynamics of winner-takes-all algorithms

Neurocomputing, 2006
Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the framework of a model situation: two competing prototype vectors are updated according to a sequence of example data drawn from a mixture of Gaussialls. The theory of on-line learning allows for an exact mathernatical description of the training dynamics, even
Biehl, Michael, Ghosh, Anarta, Hammer, Barbara   +2 more
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Noise Fuzzy Learning Vector Quantization

Key Engineering Materials, 2010
Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises).
Xiao Hong Wu, Bin Wu, Jie Wen Zhao
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