This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies...
Frederic Thouin, Mark Coates, Brian Eriksson, Robe...
The present work studies clustering from an abstract point of view and investigates its properties in the framework of inductive inference. Any class S considered is given by a hyp...
John Case, Sanjay Jain, Eric Martin, Arun Sharma, ...
This paper presents algorithms for efficiently computing the covariance matrix for features that form sub-windows in a large multidimensional image. For example, several image proc...
Stochastic context-free grammars (SCFGs) have long been recognized as useful for a large variety of tasks including natural language processing, morphological parsing, speech reco...
This paper presents a new unified subdivision scheme that is defined over a k-simplicial complex in n-D space with k 3. We first present a series of definitions to facilitate top...