Cross-domain Semi-supervised Learning Using Feature Formulation

IEEE-inst Electrical Electronics Engineers Inc
Publication Type:
Journal Article
IEEE Transactions On Systems, Man, And Cybernetics, Part B: Cybernetics, 2011, 41 (6), pp. 1627 - 1638
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011003650OK.pdf786.69 kB
Adobe PDF
Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples In this paper, sample and instance are interchangeable terms. by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Lear
Please use this identifier to cite or link to this item: