Results 291 to 300 of about 61,624 (324)

Dual Weight Learning Vector Quantization

2008 9th International Conference on Signal Processing, 2008
A 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, null Xing An, null Zhiwen Liu, null Qiangfu Zhao   +3 more
openaire   +1 more source

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 ...
DE STEFANO, Claudio, D'ELIA, Ciro, Marcelli A., SCOTTO DI FRECA, Alessandra   +3 more
openaire   +2 more sources

Expansive and Competitive Learning for Vector Quantization

Neural Processing Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Muñoz Pérez, J., Gomez Ruiz, J. A., Lopez Rubio, E., Garcia Bernal, M. A.   +3 more
openaire   +1 more source

Federated Learning Vector Quantization

ESANN 2021 proceedings, 2021
Johannes Brinkrolf, Barbara Hammer
openaire   +3 more sources

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
openaire   +1 more source

A dynamic approach to learning vector quantization

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Learning 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, D'ELIA, Ciro, A. MARCELLI   +2 more
openaire   +1 more source

Window-Based Example Selection in Learning Vector Quantization

Neural Computation, 2010
A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited.
Witoelar, A. W., Ghosh, A., de Vries, J. J. G., Hammer, Barbara, Biehl, M.   +4 more
openaire   +3 more sources

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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Viral vector platforms within the gene therapy landscape

Signal Transduction and Targeted Therapy, 2021
Phillip W L Tai, Guangping Gao
exaly  

Imaging the Néel vector switching in the monolayer antiferromagnet MnPSe3 with strain-controlled Ising order

Nature Nanotechnology, 2021
Zhuoliang Ni, Amanda V Haglund, Christian Bernhard   +2 more
exaly