A Bayesian network model is a popular technique for data mining due to its intuitive interpretation. This paper presents a semantic genetic algorithm (SGA) to learn a complete qual...
We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
Abstract— Learning techniques in robotic grasping applications have usually been concerned with the way a hand approaches to an object, or with improving the motor control of man...
Antonio Morales, Eris Chinellato, Andrew H. Fagg, ...
This paper presents and evaluates sequential instance-based learning (SIBL), an approach to action selection based upon data gleaned from prior problem solving experiences. SIBL le...
In this paper, we propose a multi-criteriabased active learning approach and effectively apply it to named entity recognition. Active learning targets to minimize the human annota...
Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, Chew L...