Results 241 to 250 of about 9,834 (275)

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

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, N B Karayiannis
exaly   +3 more sources

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

An online learning vector quantization algorithm

Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467), 2002
We propose an online learning algorithm for the learning vector quantization (LVQ) approach in nonlinear supervised classification. The advantage of this approach is the ability of the LVQ to adjust its codebook vectors as new patterns become available, so as to accurately model the class representation of the patterns. Moreover this algorithm does not
Sunil Bharitkar, Dimitar P. Filev
openaire   +1 more source

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.
Ioannis Pitas, Constantine Kotropoulos, Nikos Nikolaidis 0001, Ruikang Yang, Moncef Gabbouj   +4 more
openaire   +3 more sources

Improving Dynamic Learning Vector Quantization

18th International Conference on Pattern Recognition (ICPR'06), 2006
We introduce some improvements to the Dynamic Learning Vector Quantization algorithm proposed by us for tackling the two major problems of those networks, namely neuron over-splitting and their distribution in the feature space. We suggest to explicitly estimate the potential improvement on the recognition rate achievable by splitting neurons in those ...
Claudio De Stefano, Ciro D'Elia, Angelo Marcelli, Alessandra Scotto di Freca   +3 more
openaire   +2 more sources

Habituation in learning vector quantization

Complex Syst., 1992
Summary: A modification of Kohonen's Learning Vector Quantization [see \textit{T. Kohonen}, ``Selforganization and associative memory'' 2. ed. (1988; Zbl 0659.68100)] is proposed to handle hard cases of supervised learning with a rugged decision surface or asymmetries in the input data structure. Cell reference points (neurons) are forced to move close
Tamás Geszti, István Csabai
openaire   +2 more sources

Convergence of Kohonen's learning vector quantization

1990 IJCNN International Joint Conference on Neural Networks, 1990
It is shown that the learning vector quantization (LVQ) algorithm (T. Kohonen, 1986), converges to locally asymptotic stable equilibria of an ordinary differential equation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of
John S. Baras, Anthony LaVigna
openaire   +1 more source

Relational Extensions of Learning Vector Quantization

2011
Prototype-based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however,
Hammer, Barbara, Schleif, Frank-Michael, Zhu, Xibin, Lu, Bao-Liang, Zhang, Liqing, Kwok, James   +5 more
openaire   +1 more source

A fuzzy-soft learning vector quantization

Neurocomputing, 2003
Abstract This paper presents a batch competitive learning method called fuzzy-soft learning vector quantization (FSLVQ). The proposed FSLVQ is a batch type of clustering learning network by fusing the batch learning, soft competition and fuzzy membership functions. The comparisons between the well-known fuzzy LVQ and the proposed FSLVQ are made. In a
Kuo-Lung Wu, Miin-Shen Yang
openaire   +1 more source