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عنوان
Bayesian optimization and data science

پدید آورنده
Francesco Archetti, Antonio Candelieri

موضوع
Bayesian statistical decision theory,Data mining,Machine learning,a03,a05,a05

رده
QA279
.
5

کتابخانه
Library of College of Science University of Tehran

محل استقرار
استان: Tehran ـ شهر: Tehran

Library of College of Science University of Tehran

تماس با کتابخانه : 61112616-66495290-021

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
9783030244941

NATIONAL BIBLIOGRAPHY NUMBER

Number
E354

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Bayesian optimization and data science
First Statement of Responsibility
Francesco Archetti, Antonio Candelieri

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Cham, Switzerland
Name of Publisher, Distributor, etc.
Springer
Date of Publication, Distribution, etc.
2019

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
xiii, 126 p.
Other Physical Details
illustrations (some color)

SERIES

Series Title
(SpringerBriefs in optimization)

GENERAL NOTES

Text of Note
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

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references

SUMMARY OR ABSTRACT

Text of Note
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

TOPICAL NAME USED AS SUBJECT

Entry Element
Bayesian statistical decision theory
Entry Element
Data mining
Entry Element
Machine learning
a03
a05
a05

LIBRARY OF CONGRESS CLASSIFICATION

Class number
QA279
.
5

PERSONAL NAME - PRIMARY RESPONSIBILITY

Archetti, Francesco, 1946-

PERSONAL NAME - ALTERNATIVE RESPONSIBILITY

Candelieri, Antonio

ORIGINATING SOURCE

Country
Iran
Agency
University of Tehran. Library of College of Science

ELECTRONIC LOCATION AND ACCESS

Date and Hour of Consultation and Access
UT_SCI_BL_DB_1002497_0001.pdf

e

BL
278840

a
Y

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