Results 271 to 280 of about 61,624 (324)

Multi-stage knowledge distillation with layer fusion-based deep learning approach for skin cancer classification. [PDF]

Sci Rep
Pavel MA, Asad R, Michael GKO, Ikramuzzaman M, Mustakim M, Khan R.   +5 more
europepmc   +1 more source

High Precision Speech Keyword Spotting Based on Binary Deep Neural Network in FPGA. [PDF]

Entropy (Basel)
Zhang A, Shi J, Qian H, Wang J.
europepmc   +1 more source

Designing flexible protein structures and sampling protein conformations with a unified model using vector quantization and diffusion. [PDF]

Natl Sci Rev
Liu Y, Chen L, Chen Q, Liu H.
europepmc   +1 more source

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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
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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
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