A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
The class imbalance problem: A systematic study
Intelligent Data Analysis
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Towards pro-active adaptation with confidence: augmenting service monitoring with online testing
Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Negative training data can be harmful to text classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Predictive distribution matching SVM for multi-domain learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Multi-source domain adaptation and its application to early detection of fatigue
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
A unifying view on dataset shift in classification
Pattern Recognition
Cross-Lingual Adaptation Using Structural Correspondence Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
On the dataset shift problem in software engineering prediction models
Empirical Software Engineering
Expert Systems with Applications: An International Journal
Predicting concept changes using a committee of experts
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
A case study on meta-generalising: a Gaussian processes approach
The Journal of Machine Learning Research
Concept labeling: building text classifiers with minimal supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Domain adaptation with ensemble of feature groups
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Batch mode active sampling based on marginal probability distribution matching
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multisource domain adaptation and its application to early detection of fatigue
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
FIDOS: A generalized Fisher based feature extraction method for domain shift
Pattern Recognition
Batch Mode Active Sampling Based on Marginal Probability Distribution Matching
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Instance selection and instance weighting for cross-domain sentiment classification via PU learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Domain adaptation and sample bias correction theory and algorithm for regression
Theoretical Computer Science
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We address classification problems for which the training instances are governed by an input distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. The problem of learning under covariate shift can be written as an integrated optimization problem. Instantiating the general optimization problem leads to a kernel logistic regression and an exponential model classifier for covariate shift. The optimization problem is convex under certain conditions; our findings also clarify the relationship to the known kernel mean matching procedure. We report on experiments on problems of spam filtering, text classification, and landmine detection.