When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...
The on-line algorithms in machine learning are intended to discover unknown function of the domain based on incremental observing of it instance by instance. These algorithms have...
Helen Kaikova, Vagan Y. Terziyan, Borys Omelayenko
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
NLP tasks are often domain specific, yet systems can learn behaviors across multiple domains. We develop a new multi-domain online learning framework based on parameter combinatio...
Abstract. This paper presents a knowledge-based approach to eLearning, where the domain ontology plays central role as a resource structuring the learning content and supporting ï¬...
Galia Angelova, Ognian Kalaydjiev, Albena Strupcha...