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- W2903020166 abstract "Multi-target regression is the problem where each instance is associated with multiple continuous target outputs simultaneously. Its major challenges arise with jointly exploring the complex input-output relationships and inter-target correlations. One representative approach is to build many independent single-target Support Vector Regression (SVR) models for each output target which can capture complex input-output relationships via the kernel trick. However, it does not involve inter-target correlations to improve the performance. Meanwhile, there are also many regularization-based methods which mainly explore the linear inter-target correlations, e.g., a low-rank constraint on the parameter matrix. However, in practice, it might be restrictive to assume the targets to be linearly related, and allowing for nonlinear relationships is a challenge. Motivated by Learning Using Privileged Information (LUPI), we propose a novel privileged multi-target support vector regression (MT-PSVR) model which can jointly explore the complex input-output relationships and nonlinear inter-target correlations. It explicitly explores inter-target correlations by viewing other targets as privileged information when training each target model. Besides, it can naturally use the kernel trick to explore both the complex input-output relationships and nonlinear inter-target correlations. Experimental results on many benchmark datasets validate the effectiveness of our approach." @default.
- W2903020166 created "2018-12-11" @default.
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- W2903020166 date "2018-08-01" @default.
- W2903020166 modified "2023-09-23" @default.
- W2903020166 title "Privileged Multi-Target Support Vector Regression" @default.
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- W2903020166 doi "https://doi.org/10.1109/icpr.2018.8545479" @default.
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