標題: Titlebook: Handbook of Evolutionary Machine Learning; Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang Book 2024 The Editor(s) (if applicable) and The [打印本頁] 作者: 輕佻 時間: 2025-3-21 19:33
書目名稱Handbook of Evolutionary Machine Learning影響因子(影響力)
書目名稱Handbook of Evolutionary Machine Learning影響因子(影響力)學科排名
書目名稱Handbook of Evolutionary Machine Learning網(wǎng)絡(luò)公開度
書目名稱Handbook of Evolutionary Machine Learning網(wǎng)絡(luò)公開度學科排名
書目名稱Handbook of Evolutionary Machine Learning被引頻次
書目名稱Handbook of Evolutionary Machine Learning被引頻次學科排名
書目名稱Handbook of Evolutionary Machine Learning年度引用
書目名稱Handbook of Evolutionary Machine Learning年度引用學科排名
書目名稱Handbook of Evolutionary Machine Learning讀者反饋
書目名稱Handbook of Evolutionary Machine Learning讀者反饋學科排名
作者: 值得 時間: 2025-3-21 21:20
Wolfgang Banzhaf,Penousal Machado,Mengjie ZhangExplores various ways evolution can help improve current methods of machine learning.Presents real-world applications in medicine, robotics, science, finance, and other domains.Serves as an essential 作者: 虛弱的神經(jīng) 時間: 2025-3-22 03:48 作者: anarchist 時間: 2025-3-22 06:51
978-981-99-3816-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 即席 時間: 2025-3-22 11:25
Handbook of Evolutionary Machine Learning978-981-99-3814-8Series ISSN 1932-0167 Series E-ISSN 1932-0175 作者: IRK 時間: 2025-3-22 16:26 作者: liposuction 時間: 2025-3-22 19:32
Fundamentals of Evolutionary Machine Learningrstand evolutionary machine learning. Then we take a look at its roots, finding that it has quite a long history, going back to the 1950s. We introduce a taxonomy of the field, discuss the major branches of evolutionary machine learning, and conclude by outlining open problems.作者: 高興一回 時間: 2025-3-22 22:35
Eberhard Schaich,Alfred HamerleThis chapter provides an overview of evolutionary approaches to supervised learning. It starts with the definition and scope of the opportunity, and then reviews three main areas: evolving general neural network designs, evolving solutions that are explainable, and forming a synergy of evolutionary and gradient-based methods.作者: chronicle 時間: 2025-3-23 05:08 作者: 充氣球 時間: 2025-3-23 06:50
1932-0167 science, finance, and other domains.Serves as an essential This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this bo作者: BOAST 時間: 2025-3-23 09:42
https://doi.org/10.1007/978-3-642-71862-5ic, highlighting key approaches regarding the choice of representation and objective functions, as well as regarding the final process of model selection. Finally, we discuss successful applications of evolutionary clustering and the steps we consider necessary to encourage the uptake of these techniques in mainstream machine learning.作者: adhesive 時間: 2025-3-23 15:53 作者: 確定無疑 時間: 2025-3-23 22:01
Evolutionary Ensemble LearningL frameworks that support variable-sized ensembles, scaling to high cardinality or dimensionality, and operation under dynamic environments. Looking to the future we point out that the versatility of EEL can lead to developments that support interpretable solutions and lifelong/continuous learning.作者: 是貪求 時間: 2025-3-23 22:33 作者: Lasting 時間: 2025-3-24 04:49 作者: 小畫像 時間: 2025-3-24 06:34 作者: 貧窮地活 時間: 2025-3-24 12:34
Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline ne Optimization Tool (TPOT)?that represents pipelines as expression trees and uses genetic programming (GP) for discovery and optimization. We present some of the extensions of TPOT and its application to real-world big data. We end with some thoughts about the future of AutoML?and evolutionary machine learning.作者: 要素 時間: 2025-3-24 15:42
Book 2024chine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second作者: ANT 時間: 2025-3-24 19:09 作者: leniency 時間: 2025-3-25 01:49
Die Verteilungstheorie der Klassiker,introduce the ideas behind various evolutionary computation methods for regression and present a review of the efforts on enhancing learning capability, generalisation, interpretability?and imputation?of missing data?in evolutionary computation for regression.作者: LUCY 時間: 2025-3-25 07:10 作者: Infect 時間: 2025-3-25 08:26 作者: 錯誤 時間: 2025-3-25 13:21
Evolutionary Regression and Modellingintroduce the ideas behind various evolutionary computation methods for regression and present a review of the efforts on enhancing learning capability, generalisation, interpretability?and imputation?of missing data?in evolutionary computation for regression.作者: 褲子 時間: 2025-3-25 18:50
Evolutionary Classificationthis research area is .. This chapter introduces the fundamental concepts of evolutionary classification, followed by the key ideas using evolutionary computation techniques to address existing classification challenges such as multi-class classification, unbalanced data, explainable/interpretable classifiers and?transfer learning.作者: 具體 時間: 2025-3-25 23:56 作者: 狗舍 時間: 2025-3-26 01:33 作者: 協(xié)定 時間: 2025-3-26 07:48 作者: 分解 時間: 2025-3-26 10:14
Evolutionary Computation and the Reinforcement Learning Problemecies that rapidly adapt to environmental change and acquire new problem-solving skills through experience. Reinforcement Learning (RL) is a machine learning problem in which an agent must learn how to map situations to actions in an unknown world in order to maximise the sum of future rewards. Ther作者: 合唱隊 時間: 2025-3-26 15:04
Evolutionary Regression and Modellingry from data. Symbolic regression?goes a step further by learning explicitly symbolic models from data that are potentially interpretable. This chapter provides an overview of evolutionary computation techniques for regression and modelling including coefficient learning and symbolic regression. We 作者: 單調(diào)性 時間: 2025-3-26 18:54 作者: PANIC 時間: 2025-3-27 00:53 作者: 倔強一點 時間: 2025-3-27 03:28
Evolutionary Ensemble Learningenerally achieved by developing a diverse complement of models that provide solutions to different (yet overlapping) aspects of the task. This chapter reviews the topic of EEL by considering two basic application contexts that were initially developed independently: (1) ensembles as applied to class作者: TRAWL 時間: 2025-3-27 06:56
Evolutionary Neural Network Architecture Searches is manual, which highly relies on the domain knowledge and experience of neural networks. Neural architecture search (NAS) methods are often considered an effective way to achieve automated design of DNN architectures. There are three approaches to realizing NAS: reinforcement learning?approaches作者: 哥哥噴涌而出 時間: 2025-3-27 11:01 作者: GLUE 時間: 2025-3-27 17:35
Evolution Through Large Models applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such . (ELM), in?the main experiment ELM combined作者: 頭盔 時間: 2025-3-27 21:28
Hardware-Aware Evolutionary Approaches to Deep Neural Networks (DNN). We introduce various acceleration hardware platforms for DNNs developed especially for energy-efficient computing in edge devices. In addition to evolutionary optimization of their particular components or settings, we will describe neural architecture search (NAS)?methods adopted to directl作者: 2否定 時間: 2025-3-28 00:13
Adversarial Evolutionary Learning with Distributed Spatial Coevolutionmization–maximization problem. Different methods exist to model the search for solutions to this problem, such as the Competitive Coevolutionary Algorithm, Multi-agent Reinforcement Learning, Adversarial Machine Learning, and Evolutionary Game Theory. This chapter introduces an overview of AEL. We f作者: 瘋狂 時間: 2025-3-28 03:55 作者: Hla461 時間: 2025-3-28 08:41
Evolutionary Model Validation—An Adversarial Robustness Perspectiveize, i.e., perform well on unseen data. By properly validating a model and estimating its generalization performance, not only do we get a clearer idea of how it behaves but we might also identify problems (e.g., overfitting) before they lead to significant losses in a production environment. Model 作者: PRE 時間: 2025-3-28 12:07 作者: 約會 時間: 2025-3-28 17:14 作者: 違法事實 時間: 2025-3-28 19:57 作者: A精確的 時間: 2025-3-29 00:26
Die Verteilungstheorie der Klassiker,ry from data. Symbolic regression?goes a step further by learning explicitly symbolic models from data that are potentially interpretable. This chapter provides an overview of evolutionary computation techniques for regression and modelling including coefficient learning and symbolic regression. We 作者: 艦旗 時間: 2025-3-29 05:10 作者: Legion 時間: 2025-3-29 11:06 作者: 厭煩 時間: 2025-3-29 12:35
Wire Chamber Aging and Wire Material,enerally achieved by developing a diverse complement of models that provide solutions to different (yet overlapping) aspects of the task. This chapter reviews the topic of EEL by considering two basic application contexts that were initially developed independently: (1) ensembles as applied to class作者: 無禮回復(fù) 時間: 2025-3-29 18:52 作者: 祝賀 時間: 2025-3-29 23:01 作者: 飾帶 時間: 2025-3-30 02:26
https://doi.org/10.1007/b138751 applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such . (ELM), in?the main experiment ELM combined作者: Capitulate 時間: 2025-3-30 05:17 作者: 館長 時間: 2025-3-30 09:36
Vertically Integrated Architecturesmization–maximization problem. Different methods exist to model the search for solutions to this problem, such as the Competitive Coevolutionary Algorithm, Multi-agent Reinforcement Learning, Adversarial Machine Learning, and Evolutionary Game Theory. This chapter introduces an overview of AEL. We f作者: COW 時間: 2025-3-30 12:56 作者: 極少 時間: 2025-3-30 19:29 作者: 巨碩 時間: 2025-3-30 22:15
