Parallel learning: a perspective and a framework

The development of machine learning in complex system is hindered by two problems nowadays. The first problem is the inefficiency of exploration in state and action space, which leads to the data-hungry of some state-of-art data-driven algorithm. The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system. In this paper, we proposed a general methods that can address both two issues. We combine the concepts of descriptive learning, predictive learning, and prescriptive learning into a uniform framework, so as to build a parallel system allowing learning system improved by self-boosting. Formulating a new perspective of data, knowledge and action, we provide a new methodology called parallel learning to design machine learning system for real-world problems.