Results 201 to 210 of about 60,295 (249)

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
Seo, Sambu, Obermayer, Klaus
openaire   +3 more sources

Generalized relevance learning vector quantization

Neural Networks, 2002
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specific classification task whereby training can be ...
Hammer, Barbara, Villmann, Th.
openaire   +4 more sources

Order statistics learning vector quantizer

IEEE Transactions on Image Processing, 1996
We propose a novel class of learning vector quantizers (LVQs) based on multivariate data ordering principles. A special case of the novel LVQ class is the median LVQ, which uses either the marginal median or the vector median as a multivariate estimator of location.
Pitas, I., Kotropoulos, C., Nikolaidis, N., Yang, R., Gabbouj, M.   +4 more
openaire   +3 more sources

Hybrid learning vector quantization

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005
In this paper, a hybrid learning vector quantization algorithm is proposed. It modifies both the position of representative points and normalization parameters. Some of the experiments are operated on the synthetic and real data. The results show that the proposed hybrid learning vector quantization algorithm is applicable.
null Yuan-Cheng Lai, null Shiaw-Shian Yu, null Sheng-Lin Chou   +2 more
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

Learning Vector Quantization Networks

Substance Use & Misuse, 1998
(1998). Learning Vector Quantization Networks. Substance Use & Misuse: Vol. 33, No. 2, pp. 271-282.
openaire   +2 more sources

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
openaire   +1 more source

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

Alternative learning vector quantization

Pattern Recognition, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Kuo-Lung, Yang, Miin-Shen
openaire   +1 more source