An Algorithm for Transfer Learning in a Heterogeneous Environment
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Heterogeneous transfer learning for image clustering via the social web
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
IEEE Transactions on Knowledge and Data Engineering
The necessity of combining adaptation methods
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Predicting mental health status based on web usage behavior
AMT'11 Proceedings of the 7th international conference on Active media technology
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Computational CyberPsychology deals with web users' behaviors, and identifying their psychology characteristics using machine learning. Transfer learning intends to solve learning problems in target domain with different but related data distributions or features compared to the source domain, and usually the source domain has plenty of labeled data and the target domain doesn't. In Computational CyberPsychology, psychological characteristics of web users can't be labeled easily and cheaply, so we "borrow" labeled results of related domains by transfer learning to help us improve prediction accuracy. In this paper, we propose transfer learning for Computational CyberPsychology. We introduce Computational CyberPsychology at first, and then transfer learning, including sample selection bias and domain adaptation. We finally give a transfer learning framework for Computational CyberPsychology, and describe how it can be implemented.