Artificial Intelligence Review - Special issue on lazy learning
Machine Learning - Special issue on inductive transfer
Making large-scale support vector machine learning practical
Advances in kernel methods
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The Combination of Text Classifiers Using Reliability Indicators
Information Retrieval
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive mixtures of local experts
Neural Computation
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Efficient Bayesian task-level transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Latent space domain transfer between high dimensional overlapping distributions
Proceedings of the 18th international conference on World wide web
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross domain distribution adaptation via kernel mapping
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relaxed Transfer of Different Classes via Spectral Partition
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Proceedings of the 18th ACM conference on Information and knowledge management
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
Large margin transductive transfer learning
Proceedings of the 18th ACM conference on Information and knowledge management
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Cross validation framework to choose amongst models and datasets for transfer learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
Knowledge transfer across multilingual corpora via latent topics
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
It's who you know: graph mining using recursive structural features
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Semantic translation for rule-based knowledge in data mining
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
Source-selection-free transfer learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Triplex transfer learning: exploiting both shared and distinct concepts for text classification
Proceedings of the sixth ACM international conference on Web search and data mining
Discriminative feature selection for multi-view cross-domain learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
OMS-TL: a framework of online multiple source transfer learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
An Ensemble Model for Mobile Device based Arrhythmia Detection
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Concept learning for cross-domain text classification: a general probabilistic framework
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn. The task can be especially difficult when the training examples are from one or several domains different from the test domain. In this paper, we propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. It can integrate the advantages of various learning algorithms and the labeled information from multiple training domains into one unified classification model, which can then be applied on a different domain. Importantly, different from many previously proposed methods, none of the base learning method is required to be specifically designed for transfer learning. We show the optimality of a locally weighted ensemble framework as a general approach to combine multiple models for domain transfer. We then propose an implementation of the local weight assignments by mapping the structures of a model onto the structures of the test domain, and then weighting each model locally according to its consistency with the neighborhood structure around the test example. Experimental results on text classification, spam filtering and intrusion detection data sets demonstrate significant improvements in classification accuracy gained by the framework. On a transfer learning task of newsgroup message categorization, the proposed locally weighted ensemble framework achieves 97% accuracy when the best single model predicts correctly only on 73% of the test examples. In summary, the improvement in accuracy is over 10% and up to 30% across different problems.