東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Machine learning- Safety measures." ]

切換: 標籤 | MARC模式 | ISBD

Machine learning safety

Huang, Xiaowei.

FindBook

Google Book

Amazon

博客來

Machine learning safety

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning safety/ by Xiaowei Huang, Gaojie Jin, Wenjie Ruan.
作者:	Huang, Xiaowei.
其他作者:	Jin, Gaojie.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xvii, 321 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Safety of Simple Machine Learning Models -- 3. Safety of Deep Learning -- 4. Robustness Verification of Deep Learning -- 5. Enhancement to Robustness and Generalization -- 6. Probabilistic Graph Model -- A. Mathematical Foundations -- B. Competitions.
Contained By:	Springer Nature eBook
標題:	Machine learning - Safety measures. -
電子資源:	https://doi.org/10.1007/978-981-19-6814-3
ISBN:	9789811968143

Machine learning safety
Huang, Xiaowei.

Machine learning safety[electronic resource] /by Xiaowei Huang, Gaojie Jin, Wenjie Ruan. - Singapore :Springer Nature Singapore :2023. - xvii, 321 p. :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-306X. - Artificial intelligence: foundations, theory, and algorithms..

1. Introduction -- 2. Safety of Simple Machine Learning Models -- 3. Safety of Deep Learning -- 4. Robustness Verification of Deep Learning -- 5. Enhancement to Robustness and Generalization -- 6. Probabilistic Graph Model -- A. Mathematical Foundations -- B. Competitions.

Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.

ISBN: 9789811968143

Standard No.: 10.1007/978-981-19-6814-3doiSubjects--Topical Terms:

3630740
Machine learning
--Safety measures.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning safety
LDR:02707nmm a2200337 a 4500 001 2317032
003 DE-He213
005 20230428133604.0
006 m d
007 cr nn 008maaau
008 230902s2023 si s 0 eng d
020 $a 9789811968143 $q (electronic bk.)
020 $a 9789811968136 $q (paper)
024 7 $a 10.1007/978-981-19-6814-3 $2 doi
035 $a 978-981-19-6814-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .H874 2023
100 1 $a Huang, Xiaowei. $3 3630737
245 1 0 $a Machine learning safety $h [electronic resource] / $c by Xiaowei Huang, Gaojie Jin, Wenjie Ruan.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xvii, 321 p. : $b ill., digital ; $c 24 cm.
490 1 $a Artificial intelligence: foundations, theory, and algorithms, $x 2365-306X
505 0 $a 1. Introduction -- 2. Safety of Simple Machine Learning Models -- 3. Safety of Deep Learning -- 4. Robustness Verification of Deep Learning -- 5. Enhancement to Robustness and Generalization -- 6. Probabilistic Graph Model -- A. Mathematical Foundations -- B. Competitions.
520 $a Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
650 0 $a Machine learning $x Safety measures. $3 3630740
650 0 $a Computer security. $3 540555
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Data and Information Security. $3 3538510
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Jin, Gaojie. $3 3630738
700 1 $a Ruan, Wenjie. $3 3630739
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Artificial intelligence: foundations, theory, and algorithms. $3 2160111
856 4 0 $u https://doi.org/10.1007/978-981-19-6814-3
950 $a Computer Science (SpringerNature-11645)