The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Logistic regression with an auxiliary data source
Abstract
To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples drawn from two mismatched distributions, where Da are fully labeled and Dp partially labeled, our objective is to complete the labels of Dp. We introduce an auxiliary variable μ for each example in Da to reflect its mismatch with Dp. Under an appropriate constraint the μ's are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in Dp. The proposed algorithm, called "Migratory-Logit" or M-Logit, is demonstrated successfully on simulated as well as real data sets.
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Cites
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
Learning and evaluating classifiers under sample selection bias
Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015425 · Cited by 800
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Cited by
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
A Survey of Transfer Learning for Convolutional Neural Networks
Ribani, R., & Marengoni, M. (2019) · 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T) · doi:10.1109/sibgrapi-t.2019.00010 · Cited by 303
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Wiens, J., Guttag, J., & Horvitz, E. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002162 · Cited by 123
Transfer learning for pedestrian detection
Cao, X., Wang, Z., Yan, P., & Li, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2011.12.043 · Cited by 57
Metadata
- Authors
- Liao, X., Xue, Y., & Carin, L.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 505-512
- OpenAlex
- W2050549724
- Cited by
- 203
- References
- 3
- FWCI
- 3.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liao, X., Xue, Y., & Carin, L. (2005). Logistic regression with an auxiliary data source. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102415
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