A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
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
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
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Transferring Instances for Model-Based Reinforcement Learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
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
Text categorization with knowledge transfer from heterogeneous data sources
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
Cross-domain video concept detection: A joint discriminative and generative active learning approach
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Transfer learning aims to solve the problem that the training data from a source domain and the test data from a target domain follow different distributions. The feature-based method and the case-based method have been widely used in transfer learning. In this paper we propose a knowledge transfer method based on feature representation mapping from the source domain to the target domain. We first construct a new feature subspace, then build a feature representation mapping function and re-weight the source domain and the target domain data to minimize the distance between different distributions. As a result, with the new feature representations in this subspace, we can apply standard machine learning methods to train classifier models in the source domain for use in the target domain. Importantly, different from many previously proposed methods, we combine the feature-based method and the case-based method to construct the knowledge transfer model for solving text classification problems. The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms.