Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets.
Apolline MellotAntoine CollasSylvain ChevallierDenis A. EngemannAlexandre GramfortPublished in: CoRR (2024)
Keyphrases
- domain adaptation
- machine learning
- semi supervised
- covariate shift
- semi supervised learning
- transfer learning
- supervised learning
- labeled data
- pos tagging
- manifold alignment
- unsupervised learning
- test data
- cross domain
- multiple sources
- machine learning algorithms
- target domain
- unlabeled data
- active learning
- learning tasks
- data mining
- natural language processing
- co training
- sentiment classification
- text classification
- information extraction
- support vector machine
- training data
- learning problems
- document classification
- reinforcement learning
- data analysis
- natural language
- artificial intelligence
- decision trees
- model selection
- pairwise
- knowledge discovery
- feature selection