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ICASSP
2011
IEEE
7 years 11 months ago
Multi-view and multi-objective semi-supervised learning for large vocabulary continuous speech recognition
Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech...
Xiaodong Cui, Jing Huang, Jen-Tzung Chien
JMLR
2010
121views more  JMLR 2010»
8 years 2 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
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...
Shiliang Sun, John Shawe-Taylor
ACL
2009
8 years 5 months ago
Updating a Name Tagger Using Contemporary Unlabeled Data
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...
Cristina Mota, Ralph Grishman
ACL
2009
8 years 5 months ago
Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm
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...
Je Hun Jeon, Yang Liu
TAL
2010
Springer
8 years 5 months ago
The Effect of Semi-supervised Learning on Parsing Long Distance Dependencies in German and Swedish
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...
Anders Søgaard, Christian Rishøj
ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
8 years 5 months ago
D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification
: 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...
Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yu...
ICDM
2010
IEEE
178views Data Mining» more  ICDM 2010»
8 years 5 months ago
Exploiting Unlabeled Data to Enhance Ensemble Diversity
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...
Min-Ling Zhang, Zhi-Hua Zhou
PR
2007
205views more  PR 2007»
8 years 6 months ago
Active learning for image retrieval with Co-SVM
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...
Jian Cheng, Kongqiao Wang
ML
2002
ACM
178views Machine Learning» more  ML 2002»
8 years 6 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
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 ...
Dale Schuurmans, Finnegan Southey
ML
2000
ACM
124views Machine Learning» more  ML 2000»
8 years 7 months ago
Text Classification from Labeled and Unlabeled Documents using EM
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. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
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