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A Survey on Transfer Learning
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BibTeX
@MISC{Pan_asurvey,
author = {Sinno Jialin Pan and Qiang Yang},
title = { A Survey on Transfer Learning},
year = {}
}
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Abstract
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as co-variate shift. We also explore some potential future issues in transfer learning research.
Keyphrases
transfer learning multitask learning feature space different data distribution co-variate shift potential future issue clustering problem domain adaptation data mining algorithm transfer learning research new learning framework future data sufficient training data latter data much expensive data classification task many machine learning different feature space knowledge transfer many real-world application related machine recent year current progress sample selection bias major assumption
