Separability Index in Supervised Learning
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
We often face the situation where very limited labeled data are available to learn an effective classifier in target domain while there exist large amounts of labeled data with similar feature or distribution in certain relevant domains. Transfer learning aims at improving the performance of a learner in target domain given labeled data in one or more source domains. In this paper, we present an algorithm to learn effective classifier without or a few labeled data on target domain, given some labeled data with same features and similar distribution in source domain. Our algorithm uses the data edit technique to approach distribution from the source domain to the target domain by removing "mismatched" examples in source domain and adding "matched" examples in target domain. Experimental results on email classification problem have confirmed the effectiveness of the proposed algorithm.