In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
We present a biology-inspired probabilistic graphical model, called the hypernetwork model, and its application to medical diagnosis of disease. The hypernetwork models are a way ...
JungWoo Ha, Jae-Hong Eom, Sung-Chun Kim, Byoung-Ta...
—In this paper, we study how a humanoid robot can learn affordance relations in his environment through its own interactions in an unsupervised way. Specifically, we developed a...
Baris Akgun, Nilgun Dag, Tahir Bilal, Ilkay Atil, ...
We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned obje...
Background: Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimentin...
Lior Shamir, Nikita Orlov, D. Mark Eckley, Tomasz ...