RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
In the real world concepts are often not stable but change over time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may ...
Seppo Puuronen, Mykola Pechenizkiy, Alexey Tsymbal
Design of iterative learning control (ILC) often requires some prior knowledge about a system's control matrix. In some applications, such as uncalibrated visual servoing, th...
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Collecting large consistent data sets for real world software projects is problematic. Therefore, we explore how little data are required before the predictor performance plateaus...