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Domain adaptation from multiple sources via auxiliary classifiers
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction of patterns from the target domain by leveraging a set of pre-computed classifiers (referred to as auxiliary/source classifiers) independently learned with the labeled patterns from multiple source domains. We introduce a new datadependent regularizer based on smoothness assumption into Least-Squares
doi:10.1145/1553374.1553411
dblp:conf/icml/DuanTXC09
fatcat:nrtgo4ekkvdaboylflbhvrwwcu