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This paper discusses the main lines used to model intelligent agents that will operate in a Collaborative Online Learning Environment-COLE. The aim of COLE is to evaluate the contribution that Information Technology and ...

This study proposes and tests a dynamic online learning model designed to enhance student engagement, performance, and satisfaction. Grounded in active methodologies, gamification, and flipped learning, the model address...

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this...

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning alg...

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science a...

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solvin...