首页|AdaBoost-based transfer learning with privileged information
AdaBoost-based transfer learning with privileged information
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NSTL
Elsevier
Transfer learning aims to improve the learning of the target domain with the help of knowledge from the source domain. Recently, learning using privileged information (LUPI) has been proposed to learn an accurate classifier with privileged information which is only obtained in the training stage. In this paper, we propose a new AdaBoost-based Transfer Learning with Privileged Information (AdaTLPI) method to solve the transfer learning problem with privileged information, in which AdaBoost is taken into account to combine the weak classifiers into a strong classifier. In the model, we utilize shared parameter to transfer knowledge from the source domain to the target domain. We then incorporate privileged information about the source and target domains into a unified model and AdaBoost is used to learn a strong classifier by combining the obtained weak classifiers. Finally, we present an effective optimization algorithm to solve the proposed objective model and present the boundary of training error of the proposed method. The experimental results manifest that the proposed method can outperform the previous methods.(c) 2022 Elsevier Inc. All rights reserved.
Transfer learning (TL)Learning using privileged information (LUPI)CLASSIFICATIONKERNELTABLES
NSTL
Elsevier
