Titlebook: Inductive Logic Programming; 26th International C James Cussens,Alessandra Russo Conference proceedings 2017 Springer International Publish

forestry

流浪者

同步左右

松軟

形狀

Learning Through Advice-Seeking via Transfer,n more robust models especially in the presence of noisy and incomplete training data. Such experts are often domain but not machine learning experts. Thus, deciding what knowledge to provide is a difficult problem. Our goal is to improve the human-machine interaction by providing the expert with a

宴會

擔心

Distributional Learning of Regular Formal Graph System of Bounded Degree,t deals with term graphs instead of the terms of first-order predicate logic. We show that the regular FGS languages of bounded degree with the 1-finite context property (1-FCP) and bounded treewidth property can be learned from positive data and membership queries.

美色花錢

Learning Relational Dependency Networks for Relation Extraction,eline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction. Additionally, we demonstrate how several components such as weak supervision, . features, joint learning and the use of human advice, can be incorporated in this relational framework. We

創(chuàng)作

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