Consistency of Lipschitz learning with infinite unlabeled data and finite labeled data.
Jeff CalderPublished in: CoRR (2017)
Keyphrases
- labeled data
- unlabeled data
- labeled and unlabeled data
- supervised learning
- semi supervised learning
- semi supervised
- active learning
- learning algorithm
- co training
- domain adaptation
- text classification
- semi supervised classification
- training data
- learning problems
- sample selection bias
- supervised learning algorithms
- positive examples
- labeled instances
- labeled examples
- prior knowledge
- data points
- labeled training data
- unlabeled images
- class labels
- transfer learning
- multi view learning
- learning process
- number of labeled examples
- partially labeled data
- labeling effort
- learning tasks
- unsupervised learning
- training examples
- text categorization
- machine learning
- label propagation
- training and test data
- pairwise constraints
- learning models
- unlabeled examples
- semi supervised learning algorithms
- pairwise
- training set
- machine learning algorithms
- positive and negative
- supervised methods
- decision boundary
- text mining
- unlabeled instances
- semi supervised learning methods
- fully supervised