Learning using privileged information: SVM+ and weighted SVM

DOI10.1016/j.neunet.2014.02.002zbMath1308.68098arXiv1306.3161OpenAlexW2169408065WikidataQ43917564 ScholiaQ43917564MaRDI QIDQ2339396

Maksim Lapin, Matthias Hein, Bernt Schiele

Publication date: 1 April 2015

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

Full work available at URL: https://arxiv.org/abs/1306.3161

zbMATH Keywords

prior knowledgeSVMprivileged informationSVM+importance weightingweighted SVM

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (11)

A method to enrich experimental datasets by means of numerical simulations in view of classification tasks ⋮ Unnamed Item ⋮ Improved multi-view privileged support vector machine ⋮ Twin support vector machines with privileged information ⋮ A novel stochastic configuration network with iterative learning using privileged information and its application ⋮ KOC+: kernel ridge regression based one-class classification using privileged information ⋮ Instance weighting through data imprecisiation ⋮ Unnamed Item ⋮ A Riemannian gossip approach to subspace learning on Grassmann manifold ⋮ Unnamed Item ⋮ Unnamed Item

Uses Software

• UCI-ml
• LIBSVM

Cites Work

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• A new learning paradigm: learning using privileged information
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• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Input-dependent estimation of generalization error under covariate shift
• Sample Selection Bias as a Specification Error
• Training a Support Vector Machine in the Primal
• Estimation of Dependences Based on Empirical Data
• Choosing multiple parameters for support vector machines

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