Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on inductive transfer
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An EM Based Training Algorithm for Cross-Language Text Categorization
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
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
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
A Comparative Study of Methods for Transductive Transfer Learning
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Estimating Location Using Wi-Fi
IEEE Intelligent Systems
Can chinese web pages be classified with english data source?
Proceedings of the 17th international conference on World Wide Web
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
Bilingual topic aspect classification with a few training examples
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Transferring Knowledge from Another Domain for Learning Action Models
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Transferring localization models across space
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Transferring multi-device localization models using latent multi-task learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.