We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
The paper describes a decentralized peer-to-peer multi-agent learning method based on inductive logic programming and knowledge trading. The method uses first-order logic for model...
Within the last few years, knowledge management has become one of the hottest subjects among organisational and information systems theorists and practitioners. Many find in it an...
This investigation proposed a service-oriented approach based on a pervasive learning grid for solving the difficulties associated with sharing learning resources distributed on d...