Access the full text.
Sign up today, get DeepDyve free for 14 days.
R Chattopadhyay, Q Sun, W Fan, I Davidson, S Panchanathan, J Ye (2012)
Multisource domain adaptation and its application to early detection of fatigueACM Trans Knowl Discov Data, 6
C Han, Y-K Tan, J-H Zhu, Y Guo, J Chen, W Qing-Yao (2016)
Online feature selection of class imbalance via pa algorithmJ Comput Sci Technol, 31
SCH Hoi, J Wang, P Zhao (2014)
Libol: a library for online learning algorithmsJ Mach Learn Res, 15
SJ Pan, Q Yang (2010)
A survey on transfer learningIEEE Trans Knowl Data Eng, 22
P Zhao, SCH Hoi, J Wang, B Li (2014)
Online transfer learningArtif Intell, 216
Q Wu, MK Ng, Y Ye (2014)
Cotransfer learning using coupled markov chains with restartIEEE Intell Syst, 29
M Dredze, A Kulesza, K Crammer (2010)
Multi-domain learning by confidence-weighted parameter combinationMach Learn, 79
Y Freund, RE Schapire (1999)
Large margin classification using the perceptron algorithmMach Learn, 37
F Rosenblatt (1958)
The perceptron: a probabilistic model for information storage and organization in the brainPsychol Rev, 65
Y Freund, RE Schapire (1997)
A decision-theoretic generalization of on-line learning and an application to boostingJ Comput Syst Sci, 55
W Xindong, H Chen, W Gongqing, J Liu, Q Zheng, X He, Z-Q Zhao, B Wei, Y Li, Q Zhang (2015)
Knowledge engineering with big dataIEEE Intell Syst, 30
G Li, SCH Hoi, K Chang, W Liu, R Jain (2014)
Collaborative online multitask learningIEEE Trans Knowl Data Eng, 26
K Crammer, O Dekel, J Keshet, S Shalev-Shwartz, Y Singer (2006)
Online passive-aggressive algorithmsJ Mach Learn Res, 7
Transfer learning aims to enhance performance in a target domain by exploiting useful information from auxiliary or source domains when the labeled data in the target domain are insufficient or difficult to acquire. In some real-world applications, the data of source domain are provided in advance, but the data of target domain may arrive in a stream fashion. This kind of problem is known as online transfer learning. In practice, there can be several source domains that are related to the target domain. The performance of online transfer learning is highly associated with selected source domains, and simply combining the source domains may lead to unsatisfactory performance. In this paper, we seek to promote classification performance in a target domain by leveraging labeled data from multiple source domains in online setting. To achieve this, we propose a new online transfer learning algorithm that merges and leverages the classifiers of the source and target domain with an ensemble method. The mistake bound of the proposed algorithm is analyzed, and the comprehensive experiments on three real-world data sets illustrate that our algorithm outperforms the compared baseline algorithms.
Knowledge and Information Systems – Springer Journals
Published: Jan 11, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.