Ideal regularization for learning kernels from labels

DOI10.1016/j.neunet.2014.04.003zbMath1325.68194OpenAlexW2018967050WikidataQ45790669 ScholiaQ45790669MaRDI QIDQ889267

Binbin Pan, Li-Xin Shen, Jian Huang Lai

Publication date: 6 November 2015

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2014.04.003

zbMATH Keywords

regularizationkernel methodssemi-supervised learningvon Neumann divergencelabelsideal kernel

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Efficient learning of supervised kernels with a graph-based loss function

Uses Software

• SHOGUN
• SVMlight
• YALMIP
• SimpleMKL

Cites Work

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• Learning the kernel matrix by maximizing a KFD-based class separability criterion
• Nonlinear nonnegative matrix factorization based on Mercer kernel construction
• 10.1162/153244302760200687
• Learning Theory and Kernel Machines
• Algorithmic Learning Theory

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