Results 251 to 260 of about 9,834 (275)

Improved versions of learning vector quantization

1990 IJCNN International Joint Conference on Neural Networks, 1990
The author introduces a variant of (supervised) learning vector quantization (LVQ) and discusses practical problems associated with the application of the algorithms. The LVQ algorithms work explicitly in the input domain of the primary observation vectors, and their purpose is to approximate the theoretical Bayes decision borders using piecewise ...
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Fuzzy-Kernel Learning Vector Quantization

2004
This 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, Songcan Chen, Zhi-Hua Zhou   +2 more
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Learning Vector Quantization for Multimodal Data

2002
Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype-based clustering algorithm. Generalized relevance LVQ (GRLVQ) constitutes a modification which obeys the dynamics of a gradient descent and allows an adaptive metric utilizing relevance factors for the input dimensions.
Barbara Hammer, Marc Strickert, Thomas Villmann   +2 more
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Robust vector quantization by competitive learning

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002
Competitive neural networks can be used to efficiently quantize image and video data. We discuss a novel class of vector quantizers which perform noise robust data compression. The vector quantizers are trained to simultaneously compensate channel noise and code vector elimination noise. The training algorithm to estimate code vectors is derived by the
Joachim M. Buhmann, Thomas Hofmann 0001
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An Online Incremental Learning Vector Quantization

2009
As described in this paper, we propose online incremental learning vector quantization (ILVQ) for supervised classification tasks. As a prototype-based classifier, ILVQ needs no prior knowledge of the number of prototypes in the network or their initial value, as do most current prototype-based algorithms.
Ye Xu, Furao Shen, Osamu Hasegawa, Jinxi Zhao   +3 more
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Learning vector quantization with training data selection

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
In this paper, we propose a method that selects a subset of the training data points to update LVQ prototypes. The main goal is to conduct the prototypes to converge at a more convenient location, diminishing misclassification errors. The method selects an update set composed by a subset of points considered to be at the risk of being captured by ...
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Probabilistic learning vector quantization on manifold of symmetric positive definite matrices

Neural Networks, 2021
Fengzhen Tang, Peter Tino, Bailu Si
exaly  

UVeQFed: Universal Vector Quantization for Federated Learning

IEEE Transactions on Signal Processing, 2021
Nir Shlezinger, Mingzhe Chen, Yonina C Eldar   +2 more
exaly  

Learning Word-vector Quantization

ACM Transactions on Asian and Low-Resource Language Information Processing, 2020
Umut Orhan
exaly  

An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering

IEEE Transactions on Fuzzy Systems, 1997
N B Karayiannis, J C Bezdek
exaly