CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two c...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 29; číslo 2; s. 440 - 456
Hlavní autoři:	Yu, Yinan, Diamantaras, Konstantinos I., McKelvey, Tomas, Kung, Sun-Yuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.02.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation models Adaptive algorithms Adaptive data sampling adaptive margin Approximation algorithms between-class distance class-specific subspace Classification Computational modeling Computer applications Computer networks Data acquisition Data processing Distributed processing Hilbert space Kernel kernel approximation Kernels large scale Learning Minimization Optimization Parallel processing Representations Risk reduction sequential and parallel framework State of the art Storage capacity Subspaces support vector machine Test procedures Training Training data
ISSN:	2162-237X, 2162-2388, 2162-2388
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