Abstract. This paper introduces the use of multi-objective evolutionary algorithms in multiple instance learning. In order to achieve this purpose, a multi-objective grammar-guided...
This paper addresses the interaction between randomization, with restart strategies, and learning, an often crucial technique for proving unsatisfiability. We use instances of SAT ...
In this paper, we present automated techniques for extracting metadata instance information by organizing and mining a set of news Web sites. We develop algorithms that detect and...
Srinivas Vadrevu, Saravanakumar Nagarajan, Fatih G...
Traditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an in...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...