全部文献期刊会议图书|学者科研项目
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作者:Yuan-Yuan Shen , Yan-Ming Zhang , Xu-Yao Zhang ...
来源:[J].Neurocomputing(IF 1.634), 2020
摘要:Abstract(#br)Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning, in which the data appear in a sequential manner with a mixture of both labeled and unlabeled samples. Despite the recent advances, there are still many unsolved problems in this ...
作者:Yuan-Yuan Shen , Cheng-Lin Liu
来源:[J].Cognitive Computation(IF 0.867), 2018, Vol.10 (2), pp.334-346Springer
摘要:Abstract(#br)Incremental learning enables continuous model adaptation based on a constantly arriving data stream. It is a way relevant to human cognitive system, which learns to predict objects in a changing world. Incremental learning for character recognition is a typical scena...
作者:Fengzhen Tang , Peter Tiňo
来源:[J].Neural Networks(IF 1.927), 2017, Vol.93, pp.76-88Elsevier
摘要:... In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, lea...
作者:Brijnesh J. Jain , David Schultz
来源:[J].Pattern Recognition(IF 2.632), 2018, Vol.76, pp.349-366Elsevier
摘要:... As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed generic LVQ scheme uses asymmetric weighted averaging as update rule. We theoretically justify the asymmetric LVQ scheme via subgradient ...
作者:T. Villmann , M. Kaden , W. Hermann ...
来源:[J].Computational Statistics(IF 0.482), 2018, Vol.33 (3), pp.1173-1194Springer
摘要:Abstract(#br)This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explicitly the area under the receiver operating characteristics (ROC) curve for binary classification problems instead of the classification accuracy, which is frequently no...
作者:Johannes Brinkrolf , Christina Göpfert , Barbara Hammer
来源:[J].Neurocomputing(IF 1.634), 2019, Vol.342, pp.125-136Elsevier
摘要:Abstract(#br)Prototype-based machine learning methods such as learning vector quantisation (LVQ) offer flexible classification tools, which represent a classification in terms of typical prototypes. This representation leads to a particularly intuitive classification scheme, sinc...
作者:J.J.G. (Gert-Jan) de Vries , Steffen C. Pauws , Michael Biehl
来源:[J].Neurocomputing(IF 1.634), 2015, Vol.151, pp.873-882Elsevier
摘要:... Using Learning Vector Quantization techniques, we obtained up to 88% accuracy in the classification task to separate stress from relaxation. Relevance learning was used to identify the most informative features, indicating that most information is embedded in the cardiac sign...
作者:David Nebel , Barbara Hammer , Kathleen Frohberg ...
来源:[J].Neurocomputing(IF 1.634), 2015, Vol.169, pp.295-305Elsevier
摘要:... Most exemplar based techniques have been proposed in the unsupervised setting only, such that their performance in supervised learning tasks can be weak depending on the given data. We address the problem of learning exemplar-based models for general dissimilarity data i...
作者:Marika Kaden , Martin Riedel , Wieland Hermann ...
来源:[J].Soft Computing(IF 1.124), 2015, Vol.19 (9), pp.2423-2434Springer
摘要:Abstract(#br)Learning vector quantization (LVQ) algorithms as powerful classifier models for class discrimination of vectorial data belong to the family of prototype-based classifiers with a learning scheme based on Hebbian learning as a widely accepted neuronal learning paradigm...
作者:C.N.S. Ganesh Murthy , Y.V. Venkatesh
来源:[J].Neural Networks(IF 1.927), 1998, Vol.11 (2), pp.315-322Elsevier
摘要:...(#br)In an attempt to reduce the computational time and the size of the network, and simultaneously improve accuracy in recognition, Kohonen's learning vector quantization (LVQ) algorithm is used to train the ANN. To further improve the network's performance and to realize a n...

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