Elements of information theory
Elements of information theory
Information-based objective functions for active data selection
Neural Computation
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning from Relevant Tasks Only
ECML '07 Proceedings of the 18th European conference on Machine Learning
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
A framework for classifier adaptation and its applications in concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Transfer Learning with Data Edit
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Feature Selection by Transfer Learning with Linear Regularized Models
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Proceedings of the 18th ACM conference on Information and knowledge management
A risk minimization framework for domain adaptation
Proceedings of the 18th ACM conference on Information and knowledge management
Learning Algorithms for Domain Adaptation
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Unsupervised transfer classification: application to text categorization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
Logistic regression for transductive transfer learning from multiple sources
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Improving accuracy of microarray classification by a simple multi-task feature selection filter
International Journal of Data Mining and Bioinformatics
Localized factor models for multi-context recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Pairwise cross-domain factor model for heterogeneous transfer ranking
Proceedings of the fifth ACM international conference on Web search and data mining
Rapid pedestrian detection in unseen scenes
Neurocomputing
Active learning with transfer learning
ACL '12 Proceedings of ACL 2012 Student Research Workshop
Transfer learning for pedestrian detection
Neurocomputing
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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.