Domain Transfer Multiple Kernel Learning
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Domain adaptation transfer learning by svm suhject to a maximum mean discrepancy like constraint.
Domain transfer multiple kernel learning. Cross domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. Tsang and dong xu. 1 domain transfer multiple kernel learning lixin duan ivor w. To cope with the considerable change between feature distributions of different domains we propose a new cross domain kernel learning framework into which many existing kernel.
By lixin duan ivor w. As a research field of machine learning community transfer learning has attracted more and more attentions in recent years traditional supervised learning generally assumes that all the data including training data and unseen test data are subjected to independent and identical distribution iid which doesn t hold true under many practical circumstances especially. Domain transfer multiple kernel learning ieee trans pattern anal mach intell. Authors lixin duan 1 ivor w tsang dong xu.
Domain transfer multiple kernel learning abstract. Chen x lengellé r. Our framework referred to as domain transfer multiple kernel learning dtmkl simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Proceedings of the 6th international conference on pattern recognition applications and methods icpram 2017 2017 google scholar.
Cross domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. Cross domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. Domain transfer multiple kernel learning. Cross domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples.
Affiliation 1 nanyang technological university nanyang avenue singapore 639798.
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