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Reinforcement learning is the problem faced by an agent that must learn behaviour through trial and error interactions with a dynamic environment that lacks the educational examples. Q-learning is one of the most popular algorithms among the reinforcement learning methods. Artificial neural network, as in reinforcement learning, is a sub-entry of machine learning, which can be applied on real frames,...
This paper contains a brief description of a new computer programming tool for supervised machine learning, designed to generate production rules from data. The research tool described - named NGTS - was used for prediction of the Glasgow Outcome Scale and Rankin Scale for patients affected by severe brain damage.
Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a Learning Classifier System and data Mining Techniques. These techniques share the common factor of database dependence and there was actually little effort into making the system to understand the way human were behaving in...
Outdoor sport practitioners can improve greatly their results if they train at the right intensity. Nevertheless, in common training systems the athlete's performance is used for evaluation at the end of the exercises, and the sensed data is incomplete because only human biometrics are analyzed. These systems do not consider environmental conditions, which may have direct influence on athlete's performance...
This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data - such as location data, ontology-backed search queries, in- and outdoor conditions - the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus...
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