qYaditionally, constraint satisfaction problems(CSPs) are characterized using a finite set of constraints expressed within a common,shared constraint language. Whenreasoning acros...
The bias-variance decomposition is a very useful and widely-used tool for understanding machine-learning algorithms. It was originally developed for squared loss. In recent years,...
Much of the power of probabilistic methods in modelling language comes from their ability to compare several derivations for the same string in the language. An important starting...
Recently researchers working in the LFG framework have proposed algorithms for taking advantage of the implicit context-free components of a unification grammar [Maxwell and Kapla...
This paper presents a formal framework for designing search algorithms which can identify target images by the spatial distribution of color, edge and texture attributes. The fram...