Combining machine learning models is a means of improving overall accuracy.Various algorithms have been proposed to create aggregate models from other models, and two popular examp...
The paper presents a kernel for learning from ordered hypergraphs, a formalization that captures relational data as used in Inductive Logic Programming (ILP). The kernel generaliz...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
This paper uses a set of 3D geometric measures with the purpose of characterizing lung nodules as malignant or benign. Based on a sample of 36 nodules, 29 benign and 7 malignant, t...
In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as t...