Please use this identifier to cite or link to this item:
Title: Semi-supervised classification on evolutionary data
Authors: Jia, Y.
Yan, S. 
Zhang, C.
Issue Date: 2009
Citation: Jia, Y.,Yan, S.,Zhang, C. (2009). Semi-supervised classification on evolutionary data. IJCAI International Joint Conference on Artificial Intelligence : 1083-1088. ScholarBank@NUS Repository.
Abstract: In this paper, we consider semi-supervised classification on evolutionary data, where the distribution of the data and the underlying concept that we aim to learn change over time due to shortterm noises and long-term drifting, making a single aggregated classifier inapplicable for long-term classification. The drift is smooth if we take a localized view over the time dimension, which enables us to impose temporal smoothness assumption for the learning algorithm. We first discuss how to carry out such assumption using temporal regularizers defined in a structural way with respect to the Hilbert space, and then derive the online algorithm that efficiently finds the closed-form solution to the classification functions. Experimental results on real-world evolutionary mailing list data demonstrate that our algorithm outperforms classical semi-supervised learning algorithms in both algorithmic stability and classification accuracy.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
ISBN: 9781577354260
ISSN: 10450823
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.