標(biāo)題: Titlebook: Bayesian Optimization and Data Science; Francesco Archetti,Antonio Candelieri Book 2019 The Author(s), under exclusive license to Springer [打印本頁] 作者: CILIA 時間: 2025-3-21 18:32
書目名稱Bayesian Optimization and Data Science影響因子(影響力)
書目名稱Bayesian Optimization and Data Science影響因子(影響力)學(xué)科排名
書目名稱Bayesian Optimization and Data Science網(wǎng)絡(luò)公開度
書目名稱Bayesian Optimization and Data Science網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bayesian Optimization and Data Science被引頻次
書目名稱Bayesian Optimization and Data Science被引頻次學(xué)科排名
書目名稱Bayesian Optimization and Data Science年度引用
書目名稱Bayesian Optimization and Data Science年度引用學(xué)科排名
書目名稱Bayesian Optimization and Data Science讀者反饋
書目名稱Bayesian Optimization and Data Science讀者反饋學(xué)科排名
作者: 杠桿支點 時間: 2025-3-21 20:24
The Acquisition Function, search of the optimum towards points with potential low values of objective function either because the prediction of ., based on the probabilistic surrogate model, is low or the uncertainty, also based on the same model, is high (or both).作者: 者變 時間: 2025-3-22 04:25 作者: 省略 時間: 2025-3-22 08:28 作者: 賞心悅目 時間: 2025-3-22 09:26 作者: 急性 時間: 2025-3-22 14:47
The Acquisition Function, search of the optimum towards points with potential low values of objective function either because the prediction of ., based on the probabilistic surrogate model, is low or the uncertainty, also based on the same model, is high (or both).作者: Geyser 時間: 2025-3-22 19:32 作者: condemn 時間: 2025-3-22 23:52
SpringerBriefs in Optimizationhttp://image.papertrans.cn/b/image/181873.jpg作者: Sinus-Node 時間: 2025-3-23 03:18 作者: FOR 時間: 2025-3-23 06:33
https://doi.org/10.1007/978-3-662-05352-2What is the relation between finding the global minimum of the function below and the learning paradigm (Fig. .)? What learning models have in common with global optimization methods? Outlining possible answers and linking them to other parts of the book are the objective of this chapter.作者: 退潮 時間: 2025-3-23 10:34 作者: consent 時間: 2025-3-23 15:08 作者: 羊欄 時間: 2025-3-23 19:35 作者: 清唱劇 時間: 2025-3-23 23:49 作者: 瑣碎 時間: 2025-3-24 05:30 作者: 心胸狹窄 時間: 2025-3-24 07:20 作者: 尾巴 時間: 2025-3-24 13:40
https://doi.org/10.1007/978-3-030-24494-1Gaussian process; acquisition functions; knowledge gradient; automatic algorithm configuration; marketin作者: 尾隨 時間: 2025-3-24 16:27 作者: Overdose 時間: 2025-3-24 20:22 作者: 初次登臺 時間: 2025-3-25 02:51 作者: 生來 時間: 2025-3-25 03:20 作者: 喊叫 時間: 2025-3-25 08:00
B. A. Sullenger,R. R. White,C. P. Rusconit.?. introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect.?. presents other probabilistic models which might represent, in some cases, a suitable alternative to GP.作者: 保守 時間: 2025-3-25 13:49 作者: 背叛者 時間: 2025-3-25 17:35 作者: Between 時間: 2025-3-25 20:42
Book 2019will 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..作者: 單調(diào)女 時間: 2025-3-26 00:14 作者: 分開 時間: 2025-3-26 04:39 作者: Entreaty 時間: 2025-3-26 09:21 作者: 真實的人 時間: 2025-3-26 16:33
Software Resources,nt non-Bayesian global optimization software. The software in this section refers, basically, to the box-constrained case, with the exception of Predictive Entropy Search with Constraints (PESC) which is included in the open source package Spearmint (.).作者: 正式演說 時間: 2025-3-26 17:38
7樓作者: 赤字 時間: 2025-3-27 00:15
7樓作者: 戰(zhàn)勝 時間: 2025-3-27 03:16
7樓作者: 使隔離 時間: 2025-3-27 08:52
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