Transfer learning in evolutionary spaces

Data-driven machine learning methods usually require large amounts of annotated data to be able to develop high performance learning systems. In practical situations, such large amounts of data are not easily obtainable. Transfer Learning evolved as one ...

This paper propose to investigate a better way to apply Transfer Learning (TL) between agents to speed up the Q-learning Reinforcement Learning algorithm and combines Case-Based Reasoning (CBR) and Heuristically Accelerated Reinforcement Learning (HARL) ...

A major challenge in today's world is the Big Data problem, which manifests itself in Web and Mobile domains as rapidly changing and heterogeneous data streams. A data-mining system must be able to cope with the influx of changing data in a continual ...