Semi-supervised clustering with logic programming
Explores using logic programming and Prolog for semi-supervised clustering, arguing it's more intuitive than traditional algorithms for rule-based problems.
Explores using logic programming and Prolog for semi-supervised clustering, arguing it's more intuitive than traditional algorithms for rule-based problems.
Explores semi-supervised learning techniques for training models when labeled data is scarce, focusing on combining labeled and unlabeled data.
Explores methods like semi-supervised and active learning to create training labels when labeled datasets are unavailable, with industry examples.