東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Statistical machine learning for engineering with applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical machine learning for engineering with applications/ edited by Jürgen Franke, Anita Schöbel.
other author:	Franke, Jürgen.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	viii, 392 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	- An Introduction of Statistical Learning for Engineers -- Machine Learning for Inline Surface Inspection Systems - Challenges, Approaches, and Application Example -- Gaussian Process Regression for the Prediction of Cable Bundle Characteristics -- Machine Learning for Predictive Maintenance in Production Environments -- Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization -- Cracks in concrete -- Machine learning methods for prediction of breakthrough curves in reactive porous media -- Segmentation and Aggregation in Text Classification -- Hardware-aware Neural Architecture Search -- Optimal Experimental Design Supported by Machine Learning Regression Models -- Data Analytics, Artificial Intelligence and Machine Learning in Mobility and Vehicle Engineering.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-66253-9
ISBN:	9783031662539

Statistical machine learning for engineering with applications
Statistical machine learning for engineering with applications[electronic resource] /edited by Jürgen Franke, Anita Schöbel. - Cham :Springer Nature Switzerland :2024. - viii, 392 p. :ill. (some col.), digital ;24 cm. - Lecture notes in statistics,v. 2272197-7186 ;. - Lecture notes in statistics ;v. 227..

- An Introduction of Statistical Learning for Engineers -- Machine Learning for Inline Surface Inspection Systems - Challenges, Approaches, and Application Example -- Gaussian Process Regression for the Prediction of Cable Bundle Characteristics -- Machine Learning for Predictive Maintenance in Production Environments -- Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization -- Cracks in concrete -- Machine learning methods for prediction of breakthrough curves in reactive porous media -- Segmentation and Aggregation in Text Classification -- Hardware-aware Neural Architecture Search -- Optimal Experimental Design Supported by Machine Learning Regression Models -- Data Analytics, Artificial Intelligence and Machine Learning in Mobility and Vehicle Engineering.

This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed. The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis. The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.

ISBN: 9783031662539

Standard No.: 10.1007/978-3-031-66253-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Statistical machine learning for engineering with applications
LDR:03304nmm a2200337 a 4500 001 2380352
003 DE-He213
005 20241009130233.0
006 m d
007 cr nn 008maaau
008 250418s2024 sz s 0 eng d
020 $a 9783031662539 $q (electronic bk.)
020 $a 9783031662522 $q (paper)
024 7 $a 10.1007/978-3-031-66253-9 $2 doi
035 $a 978-3-031-66253-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S797 2024
245 0 0 $a Statistical machine learning for engineering with applications $h [electronic resource] / $c edited by Jürgen Franke, Anita Schöbel.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a viii, 392 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in statistics, $x 2197-7186 ; $v v. 227
505 0 $a - An Introduction of Statistical Learning for Engineers -- Machine Learning for Inline Surface Inspection Systems - Challenges, Approaches, and Application Example -- Gaussian Process Regression for the Prediction of Cable Bundle Characteristics -- Machine Learning for Predictive Maintenance in Production Environments -- Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization -- Cracks in concrete -- Machine learning methods for prediction of breakthrough curves in reactive porous media -- Segmentation and Aggregation in Text Classification -- Hardware-aware Neural Architecture Search -- Optimal Experimental Design Supported by Machine Learning Regression Models -- Data Analytics, Artificial Intelligence and Machine Learning in Mobility and Vehicle Engineering.
520 $a This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed. The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis. The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.
650 0 $a Machine learning. $3 533906
650 0 $a Engineering $x Statistical methods $x Data processing. $3 3733744
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 3591853
700 1 $a Franke, Jürgen. $3 3733741
700 1 $a Schöbel, Anita. $3 3733742
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in statistics ; $v v. 227. $3 3733743
856 4 0 $u https://doi.org/10.1007/978-3-031-66253-9
950 $a Mathematics and Statistics (SpringerNature-11649)