A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction.
Ya-Tong ZhouXiangyu ZhaoKuo-Ping LinChing-Hsin WangLing-Ling LiPublished in: Appl. Soft Comput. (2019)
Keyphrases
- gaussian process
- quality prediction
- learning algorithm
- gaussian processes
- image quality
- latent variables
- regression model
- gaussian process regression
- bayesian framework
- model selection
- multi task learning
- gaussian process classification
- gauss newton
- sparse approximations
- hyperparameters
- approximate inference
- semi supervised
- active learning
- feature selection
- machine learning
- mixture model
- gaussian process models
- supervised learning
- expectation propagation
- training data
- learning process
- learning tasks
- data sets
- bayesian methods
- high quality
- training set
- probabilistic model
- text mining