MDPs are an attractive formalization for planning, but realistic problems often have intractably large state spaces. When we only need a partial policy to get from a fixed start s...
H. Brendan McMahan, Maxim Likhachev, Geoffrey J. G...
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
Abstract— This paper proposes a combination of methodologies based on a recent development –called Extreme Learning Machine (ELM)– decreasing drastically the training time of...
Antti Sorjamaa, Yoan Miche, Robert Weiss, Amaury L...
Annotation of protein function often arises in the context of partially complete genomes but is not adequately addressed. We present an annotation method by extracting ortholog cl...
Akshay Vashist, Casimir A. Kulikowski, Ilya B. Muc...
A key problem in playing strategy games is learning how to allocate resources effectively. This can be a difficult task for machine learning when the connections between actions a...