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• W1988748730 abstract "Multi-output regression aims at learning a mapping from a multivariate input feature space to a multivariate output space. Despite its potential usefulness, the standard formulation of the least-squares support vector regression machine (LS-SVR) cannot cope with the multi-output case. The usual procedure is to train multiple independent LS-SVR, thus disregarding the underlying (potentially nonlinear) cross relatedness among different outputs. To address this problem, inspired by the multi-task learning methods, this study proposes a novel approach, Multi-output LS-SVR (MLS-SVR), in multi-output setting. Furthermore, a more efficient training algorithm is also given. Finally, extensive experimental results validate the effectiveness of the proposed approach." @default.
• W1988748730 created "2016-06-24" @default.
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• W1988748730 date "2013-07-01" @default.
• W1988748730 modified "2023-10-16" @default.
• W1988748730 title "Multi-output least-squares support vector regression machines" @default.
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• W1988748730 doi "https://doi.org/10.1016/j.patrec.2013.01.015" @default.
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