Regularization of unlabeled data for learning of classifiers based on mixture models
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- Type
- article
- Published
- 2009-11-01
- Cited by
- 2
- References
- 15
- OpenAlex
- https://openalex.org/W2013810580
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17191760
Keywords
Labeled data, Regularization (linguistics), Semi-supervised learning, Computer science, Artificial intelligence
References
- Pattern Classification
- Finite Mixture Models
- Finite Mixture Models
- The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Text Classification from Labeled and Unlabeled Documents using EM
- Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging
- The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter
- Learning from a mixture of labeled and unlabeled examples with parametric side information
- Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
- Maximum Likelihood from Incomplete Data via the EM Algorithm
- New algorithms for learning of mixture models and their application for classification and density estimation
- Neural Networks for Pattern Recognition
- Pattern Classification
- LBGU-EM Algorithm for Mixture Density Estimation
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