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Recurrent Learning Vector Quantization
2021Learning 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, 1996This 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, 2006Winner-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 +2 more
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Noise Fuzzy Learning Vector Quantization
Key Engineering Materials, 2010Fuzzy 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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Dual Weight Learning Vector Quantization
2008 9th International Conference on Signal Processing, 2008A new learning vector quantization (LVQ) approach, so-called dual weight learning vector quantization (DWLVQ), is presented in this paper. The basic idea is to introduce an additional weight (namely the importance vector) for each feature of reference vectors to indicate the importance of this feature during the classification.
null Chuanfeng Lv +3 more
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Improving Dynamic Learning Vector Quantization
18th International Conference on Pattern Recognition (ICPR'06), 2006We 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 ...
DE STEFANO, Claudio +3 more
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Expansive and Competitive Learning for Vector Quantization
Neural Processing Letters, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Muñoz Pérez, J. +3 more
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Federated Learning Vector Quantization
ESANN 2021 proceedings, 2021Johannes Brinkrolf, Barbara Hammer
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Fuzzy-Kernel Learning Vector Quantization
2004This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures.
Daoqiang Zhang +2 more
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A dynamic approach to learning vector quantization
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004Learning 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.
DE STEFANO, Claudio +2 more
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