目录

●《材料信息学导论(中) 高等机器学习 第一册》
Part A Optimization and Learning Strategies
1 Bayesian Global Optimization 3
1.1 Gaussian Process Regression (GPR), Ordinary Kriging (OK), and Jones Approach 6
1.2 Acquisition Functions 31
1.2.1 Expected Improvement (EI) 31
1.2.2 Expected Improvement with Plugin (EI Plugin) 44
1.2.3 The Reinterpolation Procedure 47
1.2.4 Augmented Expected Improvement (AEI) 51
1.2.5 Upper Confidence Bound (UCB) and Minimum Quantile (MQ) 59
1.2.6 Probability of Improvement (PI or PoI) 66
1.2.7 Expected Quantile Improvement (EQI) 73
1.2.8 Knowledge Gradient (KG) 83
1.2.9 Entropy Search (ES) 94
1.2.10 Utility Functions and Acquisition Functions for Classification 99
1.3 Multiobjective Optimization 108
1.3.1 Classical Methods for Multiobjective Optimization 110
1.3.2 Nondominated Feature Points (Solutions) and Pareto Front (Srinivas and Deb 1791–1994) 114
1.3.3 Hypervolume Improvement 128
References 150
2 Swarm-Based Optimization Algorithms 155
2.1 Particle Swarm Optimization (PSO) 155
2.2 Whale Optimization Algorithm (WOA) 165
2.2.1 Shrinking Encircling Method 167
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《材料信息学导论(中) 高等机器学习 第二册》