ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech...
JMLR
2010
12 years 11 months ago
2010
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
ACL
2009
13 years 2 months ago
2009
For many NLP tasks, including named entity tagging, semi-supervised learning has been proposed as a reasonable alternative to methods that require annotating large amounts of trai...
ACL
2009
13 years 2 months ago
2009
Most of previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic lab...
TAL
2010
Springer
13 years 2 months ago
2010
Springer
This paper shows how the best data-driven dependency parsers available today [1] can be improved by learning from unlabeled data. We focus on German and Swedish and show that label...
ICDM
2010
IEEE
13 years 2 months ago
2010
IEEE
: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi HP Labo...
ICDM
2010
IEEE
13 years 2 months ago
2010
IEEE
Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate a...
PR
2007
13 years 4 months ago
2007
In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning alg...
ML
2002
ACM
13 years 4 months ago
2002
ACM
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
ML
2000
ACM
13 years 4 months ago
2000
ACM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...