We propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature s is more important than feature t. Our clustering formulati...
Jun Sun, Wenbo Zhao, Jiangwei Xue, Zhiyong Shen, Y...
In this paper we introduce three alternative combinatorial formulations of the theory of evidence (ToE), by proving that both plausibility and commonality functions share the stru...
The accuracy of methods for the assessment of mammographic risk analysis is heavily related to breast tissue characteristics. Previous work has demonstrated considerable success i...
Neil MacParthalain, Richard Jensen, Qiang Shen, Re...
Insufficient training data is one of the major problems in neural network learning, because it leads to poor learning performance. In order to enhance an intelligent learning proc...
Stacking is a widely used technique for combining classifiers and improving prediction accuracy. Early research in Stacking showed that selecting the right classifiers, their par...
According to Koestler, the notion of a bisociation denotes a connection between pieces of information from habitually separated domains or categories. In this paper, we consider a ...
Data-driven knowledge discovery is becoming a new trend in various scientific fields. In light of this, the goal of the present paper is to introduce a novel framework to study one...
Chen Yu, Thomas G. Smith, Shohei Hidaka, Matthias ...
Abstract. Reservoir computing approaches have been successfully applied to a variety of tasks. An inherent problem of these approaches, is, however, their variation in performance ...
Abstract. In this paper, we propose a novel approach for adaptive control of robotic manipulators. Our approach uses a representation of inverse dynamics models learned from a vari...