Knowledge transfer via multiple model local structure mapping

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 a...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Jing Gao, Wei Fan, Jing Jiang, Jiawei Han
• Keywords: Data Mining, KDD 2008, Test Examples, Transfer Learning Task, Weighted Ensemble Framework