Background: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm tha...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Estimation and tracking of generally nonstationary Markov processes is of paramount importance for applications such as localization and navigation. In this context, ad hoc wireles...
Eric J. Msechu, Stergios I. Roumeliotis, Alejandro...
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...