A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
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
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Set-Based Boosting for Instance-Level Transfer
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Selective knowledge transfer for machine learning
Selective knowledge transfer for machine learning
IEEE Transactions on Knowledge and Data Engineering
Multi-resolution boosting for classification and regression problems
Knowledge and Information Systems
Over-Sampling from an auxiliary domain
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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Instance-based transfer learning methods utilize labeled examples from one domain to improve learning performance in another domain via knowledge transfer. Boosting-based transfer learning algorithms are a subset of such methods and have been applied successfully within the transfer learning community. In this paper, we address some of the weaknesses of such algorithms and extend the most popular transfer boosting algorithm, TrAdaBoost. We incorporate a dynamic factor into TrAdaBoost to make it meet its intended design of incorporating the advantages of both AdaBoost and the "Weighted Majority Algorithm". We theoretically and empirically analyze the effect of this important factor on the boosting performance of TrAdaBoost and we apply it as a "correction factor" that significantly improves the classification performance. Our experimental results on several real-world datasets demonstrate the effectiveness of our framework in obtaining better classification results.