國立虎尾科技大學 |

Bayesian Optimization and Data Science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian Optimization and Data Science / by Francesco Archetti, Antonio Candelieri.
作者:	Archetti, Francesco.
其他作者:	Candelieri, Antonio.
面頁冊數:	XIII, 126 p. 52 illus., 39 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Operations research. -
電子資源:	https://doi.org/10.1007/978-3-030-24494-1
ISBN:	9783030244941

Bayesian Optimization and Data Science
Archetti, Francesco.

Bayesian Optimization and Data Science [electronic resource] /by Francesco Archetti, Antonio Candelieri. - 1st ed. 2019. - XIII, 126 p. 52 illus., 39 illus. in color.online resource. - SpringerBriefs in Optimization,2190-8354. - SpringerBriefs in Optimization,.

1. Automated Machine Learning and Bayesian Optimization -- 2. From Global Optimization to Optimal Learning -- 3. The Surrogate Model -- 4. The Acquisition Function -- 5. Exotic BO -- 6. Software Resources -- 7. Selected Applications.

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.

ISBN: 9783030244941

Standard No.: 10.1007/978-3-030-24494-1doiSubjects--Topical Terms:

573517
Operations research.

LC Class. No.: QA402-402.37

Dewey Class. No.: 519.6

Bayesian Optimization and Data Science
LDR:02553nam a22004215i 4500 001 1012975
003 DE-He213
005 20200702005914.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030244941 $9 978-3-030-24494-1
024 7 $a 10.1007/978-3-030-24494-1 $2 doi
035 $a 978-3-030-24494-1
050 4 $a QA402-402.37
050 4 $a T57.6-57.97
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJM $2 thema
082 0 4 $a 519.6 $2 23
100 1 $a Archetti, Francesco. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1307283
245 1 0 $a Bayesian Optimization and Data Science $h [electronic resource] / $c by Francesco Archetti, Antonio Candelieri.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XIII, 126 p. 52 illus., 39 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Optimization, $x 2190-8354
505 0 $a 1. Automated Machine Learning and Bayesian Optimization -- 2. From Global Optimization to Optimal Learning -- 3. The Surrogate Model -- 4. The Acquisition Function -- 5. Exotic BO -- 6. Software Resources -- 7. Selected Applications.
520 $a 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.
650 0 $a Operations research. $3 573517
650 0 $a Management science. $3 719678
650 0 $a Machine learning. $3 561253
650 0 $a Computer software. $3 528062
650 0 $a Statistics . $3 1253516
650 1 4 $a Operations Research, Management Science. $3 785065
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Mathematical Software. $3 672446
650 2 4 $a Bayesian Inference. $3 1211345
700 1 $a Candelieri, Antonio. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1307284
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030244934
776 0 8 $i Printed edition: $z 9783030244958
830 0 $a SpringerBriefs in Optimization, $x 2190-8354 $3 1254063
856 4 0 $u https://doi.org/10.1007/978-3-030-24494-1
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)