Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning

Abstract. One solution to the lack of label problem is to exploit transfer learning, whereby one acquires knowledge from source-domains to improve the learning performance in the target-domain. The main challenge is that the source and target domains may have different distributions. An open problem is how to select the available models (including algorithms and parameters) and importantly, abundance of source-domain data, through statistically reliable methods, thus making transfer learning practical and easy-to-use for real-world applications. To address this challenge, one needs to take into account the difference in both marginal and conditional distributions in the same time, but not just one of them. In this paper, we formulate a new criterion to overcome “double” distribution shift and present a practical approach “Transfer Cross Validation” (TrCV) to select both models and data in a cross validation framework, optimized for transfer learning. The idea is to use densit...

• Type: Journal
• Year: 2010
• Where: PKDD
• Authors: ErHeng Zhong, Wei Fan, Qiang Yang, Olivier Verscheure, Jiangtao Ren
• Keywords: Conditional Distribution, Data Mining, Marginal Distribution, PKDD 2010, Transfer Learning