Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
This paper proposes an approach to the problem of generating metadata for composite mixed-media digital objects by appropriately combining and exploiting existing knowledge or met...
In this paper, we propose a new machine learning approach based on AFS (Axiomatic Fuzzy Sets) fuzzy logic, in attempt to provide a better model with interpretability. First, we wil...
Whereas schools typically record mounds of data regarding student performance, attendance, and other behaviors over the course of a school year, rarely is that data consulted and ...
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that ...