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Displaying 1-7 out of 7 total
Supervised Self-taught Learning: Actively transferring knowledge from unlabeled data
Found in: Neural Networks, IEEE - INNS - ENNS International Joint Conference on
By Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, Colin Campbell
Issue Date:June 2009
pp. 1272-1277
We consider the task of Self-taught Learning (STL) from unlabeled data. In contrast to semi-supervised learning, which requires unlabeled data to have the same set of class labels as labeled data, STL can transfer knowledge from different types of unlabele...
 
Semi-supervised Learning from General Unlabeled Data
Found in: Data Mining, IEEE International Conference on
By Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu
Issue Date:December 2008
pp. 273-282
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better utilize the information from unlabeled data by formulating them as a three-c...
 
Bayesian Nonparametric Models for Multiway Data Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Zenglin Xu,Feng Yan,Yaun Qi
Issue Date:July 2014
pp. 1
Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches--such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)--amount to multi-linear factorization. They are insufficient to mode...
 
Bayesian Nonparametric Models for Multiway Data Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Zenglin Xu,Feng Yan,Yaun Qi
Issue Date:October 2013
pp. 1
Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches--such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)--amount to multi-linear factorization. They are insufficient to mode...
 
Non-monotonic feature selection
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Irwin King, Jieping Ye, Michael R. Lyu, Rong Jin, Zenglin Xu
Issue Date:June 2009
pp. 1-8
We consider the problem of selecting a subset of m most informative features where m is the number of required features. This feature selection problem is essentially a combinatorial optimization problem, and is usually solved by an approximation. Conventi...
     
Semi-supervised text categorization by active search
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Irwin King, Kaizhu Huang, Michael R. Lyu, Rong Jin, Zenglin Xu
Issue Date:October 2008
pp. 1001-1001
In automated text categorization, given a small number of labeled documents, it is very challenging, if not impossible, to build a reliable classifier that is able to achieve high classification accuracy. To address this problem, a novel web-assisted text ...
     
Web page classification with heterogeneous data fusion
Found in: Proceedings of the 16th international conference on World Wide Web (WWW '07)
By Irwin King, Michael R. Lyu, Zenglin Xu
Issue Date:May 2007
pp. 1171-1172
Web pages are more than text and they contain much contextual and structural information, e.g., the title, the meta data, the anchor text,etc., each of which can be seen as a data source or are presentation. Due to the different dimensionality and differen...
     
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