03369nam 2200529 450 99654796370331620230614053059.09789811968143(electronic bk.)978981196813610.1007/978-981-19-6814-3(MiAaPQ)EBC7242968(Au-PeEL)EBL7242968(DE-He213)978-981-19-6814-3(OCoLC)1378610667(PPN)269657614(EXLCZ)992655787490004120230614d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning Safety /Xiaowei Huang, Gaojie Jin, and Wenjie Ruan1st ed. 2023.Singapore :Springer Nature Singapore Pte Ltd.,[2023]©20231 online resource (319 pages)Artificial Intelligence: Foundations, Theory, and Algorithms SeriesPrint version: Huang, Xiaowei Machine Learning Safety Singapore : Springer,c2023 9789811968136 Includes bibliographical references.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.Artificial Intelligence: Foundations, Theory, and Algorithms,2365-306XComputer securityMachine learningSafety measuresComputer security.Machine learningSafety measures.005.8Huang Xiaowei1355348Jin GaojieRuan WenjieMiAaPQMiAaPQMiAaPQ996547963703316Machine Learning Safety3359461UNISA