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Bayesian Optimization and Data Science 2019 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 126 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 39 Illustrations, color; 13 Illustrations, black and white; XIII, 126 p. 52 illus., 39 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Optimization
  • Ilmumisaeg: 07-Oct-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030244938
  • ISBN-13: 9783030244934
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Bayesian Optimization and Data Science 2019 ed.Suurem pilt
  • Formaat: Paperback / softback, 126 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 39 Illustrations, color; 13 Illustrations, black and white; XIII, 126 p. 52 illus., 39 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Optimization
  • Ilmumisaeg: 07-Oct-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030244938
  • ISBN-13: 9783030244934
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030244934.html
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. 





The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
1 Automated Machine Learning and Bayesian Optimization
1(18)
1.1 Automated Machine Learning
1(7)
1.1.1 Motivation
1(2)
1.1.2 Model Selection
3(1)
1.1.3 Hyperparameter Optimization
4(2)
1.1.4 Combined Algorithm Selection and Hyperparameter Optimization
6(1)
1.1.5 Why Hyperparameter Optimization Is Important?
6(2)
1.2 The Basic Structure of Bayesian Optimization
8(6)
1.2.1 Sequential Model-Based Optimization
8(2)
1.2.2 Surrogate Model
10(2)
1.2.3 Acquisition Function
12(2)
1.3 Automated Machine Learning for Predictive Analytics
14(3)
References
17(2)
2 From Global Optimization to Optimal Learning
19(18)
2.1 A Priori Analysis of Global Optimization Strategies
20(1)
2.2 Lipschitz Global Optimization (LGO)
21(3)
2.3 Random Search
24(4)
2.3.1 General Properties of Uniform Sampling
26(1)
2.3.2 Cluster Analysis
26(1)
2.3.3 Stopping Rules
26(2)
2.4 Bandits, Active Learning and Bayesian Optimization
28(4)
References
32(5)
3 The Surrogate Model
37(20)
3.1 Gaussian Processes
37(10)
3.1.1 Gaussian Processes Regression
37(3)
3.1.2 Kernel: The Data Geometry of Bayesian Optimization
40(3)
3.1.3 Embedding Derivative Observations in the Gaussian Process
43(3)
3.1.4 Numerical Instability
46(1)
3.2 Thompson Sampling
47(3)
3.3 Alternative Models
50(5)
3.3.1 Random Forest
51(2)
3.3.2 Neural Networks: Feedforward, Deep and Bayesian
53(2)
References
55(2)
4 The Acquisition Function
57(16)
4.1 Traditional Acquisition Functions
57(5)
4.1.1 Probability of Improvement
57(1)
4.1.2 Expected Improvement
58(2)
4.1.3 Upper/Lower Confidence Bound
60(2)
4.2 New Acquisition Functions
62(8)
4.2.1 Scaled Expected Improvement
62(1)
4.2.2 Portfolio Allocation
62(1)
4.2.3 Thompson Sampling
63(2)
4.2.4 Entropy-Based Acquisition Functions
65(1)
4.2.5 Knowledge Gradient
66(2)
4.2.6 Look-Ahead
68(1)
4.2.7 K-Optimality
69(1)
4.3 Optimizing the Acquisition Function
70(1)
References
71(2)
5 Exotic Bayesian Optimization
73(24)
5.1 Constrained Global Optimization
73(3)
5.2 Support Vector Machine---Constrained Bayesian Optimization
76(10)
5.3 Safe Bayesian Optimization
86(4)
5.4 Parallel Bayesian Optimization
90(1)
5.5 Multi-objective Bayesian Optimization
91(2)
5.6 Multi-source and Multi-fidelity Bayesian Optimization
93(1)
References
94(3)
6 Software Resources
97(14)
6.1 Open Source Software
97(4)
6.2 Bayesian Optimization as a Service
101(1)
6.3 Bayesian Optimization-Based Services for Hyperparameters Optimization
102(1)
6.4 Test Functions and Generators
103(4)
6.4.1 Survey and Site/Repository of Test Functions
103(4)
6.4.2 Test Functions Generators
107(1)
6.5 Non-Bayesian Global Optimization Software
107(1)
References
108(3)
7 Selected Applications
111
7.1 Overview of Applications
111(5)
7.2 Smart Water
116(6)
7.2.1 Leakage Localization
116(2)
7.2.2 Pump Scheduling Optimization
118(4)
References
122