9789811968167 - machine learning safety (artificial intelligence: foundations, theory, and algorithms) von huang, xiaowei; jin, gaojie; ruan, wenjie (2 Ergebnisse)
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Sprache: Englisch
Verlag: Springer, 2024
Serie: Buch 13 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
- Softcover
Anbieter: preigu, Osnabrück, Deutschlandpreigu
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EUR 54,90
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Taschenbuch. Zustand: Neu. Machine Learning Safety | Xiaowei Huang (u. a.) | Taschenbuch | Artificial Intelligence: Foundations, Theory, and Algorithms | xvii | Englisch | 2024 | Springer | EAN 9789811968167 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]sp…ringer[dot]com | Anbieter: preigu.
- Weitere Bilder
Sprache: Englisch
Verlag: Springer, Springer, 2024
Serie: Buch 13 von 15 - Artificial Intelligence: Foundations, Theory, and Algorithms
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 63,87
EUR 62,59 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 d…evelopment 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.
