Abstract
In real applications, labeled instances are often deficient which makes the classification problem on the target task difficult. To solve this problem, transfer learning techniques are introduced to make use of existing knowledge from the source data sets to the target data set. However, due to the discrepancy of distributions between tasks, directly transferring knowledge will possibly lead to degenerated performance which is also called negative trasnfer. In this paper, we adopted the Gaussian process to alleviate this problem by directly evaluating the distribution differences, with the parameter-free Minimum Description Length Principle (MDLP) for encoding. The proposed method inherits the good property of solid theoretical foundation as well as noise-tolerance. Extensive experiments results show the effectiveness of our method.
This work was supported by Humanity and Social Science Youth foundation of Ministry of Education of China (No. 13YJC630126), the Fundamental Research Funds for the Central Universities (No.WK0110000032), the NSFC (No.71171184/71201059/71201151), the Funds for Creative Research Group of China (No. 70821001) and the NSFC major program (No.71090401/71090400).
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Shao, H., Xu, R., Tao, F. (2013). Gaussian Process for Transfer Learning through Minimum Encoding. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_47
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DOI: https://doi.org/10.1007/978-3-642-41278-3_47
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