Learning a mixture model for clustering with the completed likelihood minimum message length criterion.
Hong ZengYiu-ming CheungPublished in: Pattern Recognit. (2014)
Keyphrases
- minimum message length
- unsupervised learning
- mixture model
- finite mixtures
- expectation maximization
- em algorithm
- density estimation
- maximum likelihood
- model selection
- learning algorithm
- gaussian mixture model
- learning process
- supervised learning
- probabilistic model
- data sets
- mixture modeling
- reinforcement learning
- bayesian information criterion
- clustering algorithm
- active learning
- text classification
- feature space