Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Abstract. This paper describes a new approach for parameter optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the new ...
In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation ...
Abstract—This paper presents a novel zero-sum watermarking game between a detection algorithm and a data hiding adversary. Contrary to previous research, the detection algorithm ...
Abstract. Three theoretical perspectives upon conservation of performance in function optimization are outlined. In terms of statistical information, performance is conserved when ...