… Research on distributed reinforcement learning theory and its applications in multirobot
systems［D］. Harbin: College of Computer Science and Technology, Harbin Engineering …

EXECUTIVE SUMMARY This chapter examines Chinese learning from other peoples’ wars
and military transformation and the impact of this learning on China’s military thoughts, …

… on data mining and machine learning, is a classification problem. It comes from a study of …
information may be missing, information on key variables may be missing, high quality …

… of war” data obtained on the battlefield is incomplete and opaque, it is likely to receive data
… ‘The integration of AI, machine learning, and big data analytics can significantly improve the …

… The deployment of these machine learning models in clinical settings … In conclusion, machine
learning algorithms show great … Missing Values: Assess the extent and patterns of missing …

… Machine learning represents a growing subfield of artificial intelligence with much promise
… approaches, as reinforcement learning is currently not well-suited for medical analyses (Choi …

… Reinforcement learning (RL) is a branch of machine learning where an agent learns to … for
analysis. With big data, the volume, variety, and velocity of data can introduce noise, missing …

… Starting with an introduction to time series, it elaborates on statistical, machine learning,
and hybrid approaches for time series forecasting in the course of this chapter. Furthermore, it …

… This article explores the effectiveness of longitudinal data analysis, artificial intelligence,
and machine learning approaches based on magnetic resonance imaging and positron …

… machine learning, respectively. Lastly, this survey presents the prospects of network disintegration
study … Optimal disintegration strategy with heterogeneous costs in complex networks [J…