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» Learning from Labeled and Unlabeled Data Using Random Walks
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COLING
2008
13 years 9 months ago
Extractive Summarization Using Supervised and Semi-Supervised Learning
It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. They are cat...
Kam-Fai Wong, Mingli Wu, Wenjie Li
SIGIR
2009
ACM
14 years 2 months ago
Extracting structured information from user queries with semi-supervised conditional random fields
When search is against structured documents, it is beneficial to extract information from user queries in a format that is consistent with the backend data structure. As one step...
Xiao Li, Ye-Yi Wang, Alex Acero
MVA
2007
13 years 9 months ago
Semi-supervised Incremental Learning of Manipulative Tasks
For a social robot, the ability of learning tasks via human demonstration is very crucial. But most current approaches suffer from either the demanding of the huge amount of label...
Zhe Li, Sven Wachsmuth, Jannik Fritsch, Gerhard Sa...
WAIM
2009
Springer
14 years 13 days ago
Kernel-Based Transductive Learning with Nearest Neighbors
In the k-nearest neighbor (KNN) classifier, nearest neighbors involve only labeled data. That makes it inappropriate for the data set that includes very few labeled data. In this ...
Liangcai Shu, Jinhui Wu, Lei Yu, Weiyi Meng
ICASSP
2011
IEEE
12 years 11 months ago
Exploiting active-learning strategies for annotating prosodic events with limited labeled data
Many applications of spoken-language systems can benefit from having access to annotations of prosodic events. Unfortunately, obtaining human annotations of these events, even se...
Raul Fernandez, Bhuvana Ramabhadran