KOC+: kernel ridge regression based one-class classification using privileged information

DOI10.1016/J.INS.2019.07.052zbMath1459.62139OpenAlexW2961858021WikidataQ127477336 ScholiaQ127477336MaRDI QIDQ2225166

Yanyan Li

Publication date: 5 February 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2019.07.052

zbMATH Keywords

one-class classificationkernel learningkernel ridge regression (KRR)learning using privileged information (LUPI)

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Twin support vector machines with privileged information ⋮ R-CTSVM+: robust capped \(\mathrm{L}_1\)-norm twin support vector machine with privileged information ⋮ A high-order norm-product regularized multiple kernel learning framework for kernel optimization ⋮ A novel stochastic configuration network with iterative learning using privileged information and its application

Uses Software

• UCI-ml
• GitHub

Cites Work

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• A new learning paradigm: learning using privileged information
• One-class classification with extreme learning machine
• Coupling privileged kernel method for multi-view learning
• Learning using privileged information: SVM+ and weighted SVM

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