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AAAI
2011
12 years 7 months ago
Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been fou...
Yu-Feng Li, Zhi-Hua Zhou
TKDE
2012
245views Formal Methods» more  TKDE 2012»
11 years 10 months ago
Semi-Supervised Maximum Margin Clustering with Pairwise Constraints
—The pairwise constraints specifying whether a pair of samples should be grouped together or not have been successfully incorporated into the conventional clustering methods such...
Hong Zeng, Yiu-ming Cheung
SDM
2011
SIAM
269views Data Mining» more  SDM 2011»
12 years 10 months ago
Semi-Supervised Convolution Graph Kernels for Relation Extraction
Extracting semantic relations between entities is an important step towards automatic text understanding. In this paper, we propose a novel Semi-supervised Convolution Graph Kerne...
Xia Ning, Yanjun Qi
IAT
2010
IEEE
13 years 5 months ago
Semi-supervised Learning for Opinion Detection
Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions. However, opinion-labeled data, especially at the su...
Ning Yu, Sandra Kübler
ICML
2006
IEEE
14 years 8 months ago
A continuation method for semi-supervised SVMs
Semi-Supervised Support Vector Machines (S3 VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do n...
Olivier Chapelle, Mingmin Chi, Alexander Zien