We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for l...
This paper investigates the problem of learning the visual semantics of keyword categories for automatic image annotation. Supervised learning algorithms which learn only a single ...
Abstract. We present a new method to break symmetry in graph coloring problems. While most alternative techniques add symmetry breaking predicates in a pre-processing step, we deve...
Abstract. This paper is concerned with generalization issues for a decision tree learner for structured data called Alkemy. Motivated by error bounds established in statistical lea...
Over the last 25 years there has been considerable body of research into combinations of predicate logic and probability forming what has become known as (perhaps misleadingly) sta...