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Titlebook: Automated Design of Machine Learning and Search Algorithms; Nelishia Pillay,Rong Qu Book 2021 Springer Nature Switzerland AG 2021 Machine

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期刊全稱Automated Design of Machine Learning and Search Algorithms
影響因子2023Nelishia Pillay,Rong Qu
視頻videohttp://file.papertrans.cn/167/166286/166286.mp4
發(fā)行地址Presents recent advances across automated machine learning and automated algorithm design.Contains a useful introduction to the fast-developing area of automated design of machine learning.Includes co
學科分類Natural Computing Series
圖書封面Titlebook: Automated Design of Machine Learning and Search Algorithms;  Nelishia Pillay,Rong Qu Book 2021 Springer Nature Switzerland AG 2021 Machine
影響因子.This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. ..The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by e
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Rigorous Performance Analysis of Hyper-heuristics,orithms aim at automating the optimisation process by using a set of low-level heuristics and a machine learning mechanism to decide online which heuristic is the most appropriate one at the current stage. We mainly focus on work that establishes the performance gains that simple and sophisticated h
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Automated Design of Classification Algorithms,inancial forecasting, among others. Classification algorithms such as induction algorithms, e.g., ID3, and genetic programming are used to produce classifiers. The design of these classification algorithms is time-consuming, requiring many person hours, and is an optimization problem. This chapter e
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