This paper presents a graph-theoretic model of the acquisition of lexical syntactic representations. The representations the model learns are non-categorical or graded. We propose...
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
In this paper we deal with knowledge representation in the area of learning design and adaptive learning. Specification of concrete instances is usually context-dependent and does...
A powerful approach to search is to try to learn a distribution of good solutions (in particular of the dependencies between their variables) and use this distribution as a basis ...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...