Issue No. 04 - July-Aug. (2014 vol. 29)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIS.2013.32
Qingyao Wu , Harbin Institute of Technology
Michael K. Ng , Hong Kong Baptist University
Yunming Ye , Harbin Institute of Technology
This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.
Machine learning, Markov processes, Training data, Ranking, Classification algorithms, Learning systems, Iterative methods,intelligent systems, cotransfer learning, transfer learning, coupled Markov chains, classification, labels ranking, iterative methods
Qingyao Wu, Michael K. Ng, Yunming Ye, "Cotransfer Learning Using Coupled Markov Chains with Restart", IEEE Intelligent Systems, vol. 29, no. , pp. 26-33, July-Aug. 2014, doi:10.1109/MIS.2013.32