We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed...
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K...
— We consider learning in a transductive setting using instance-based learning (k-NN) and present a method for constructing a data-dependent distance “metric” using both labe...
Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement l...
This paper reviews the recent surge of interest in convex optimization in a context of pattern recognition and machine learning. The main thesis of this paper is that the design of...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
Learning based super-resolution can recover high resolution image with high quality. However, building an interactive learning based super-resolution system for general images is e...