Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
We present a model of recursive and impredicatively quantified types with mutable references. We interpret in this model all of the type constructors needed for typed intermediate...
Andrew W. Appel, Christopher D. Richards, Jé...
There is a growing demand for network devices capable of examining the content of data packets in order to improve network security and provide application-specific services. Most...
In recent work Baader has shown that a certain description logic with conjunction, existential quantification and with circular definitions has a polynomial time subsumption pro...
ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens ...