Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM).
Ziang LiuHanbin LuoWeili FangJiajing LiuDongrui WuPublished in: CoRR (2020)
Keyphrases
- active learning
- semi supervised
- supervised learning
- active sampling
- regression model
- regression algorithm
- machine learning
- objective function
- selective sampling
- random sampling
- unsupervised learning
- learning algorithm
- active learner
- learning strategies
- data driven
- linear regression
- training examples
- regression analysis
- sample selection
- support vector regression
- labeled data
- simple linear
- regression method
- model selection
- unlabeled data
- support vector
- feature selection
- regression function
- training set
- learning process
- semi supervised learning
- iterative methods
- regression methods
- experimental design
- gaussian processes
- generalization error
- genetic programming
- batch mode
- sample complexity
- ridge regression
- locally weighted
- logistic regression
- search result diversification
- crowd sourced
- query by committee
- pool based active learning
- imbalanced data classification