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      Machine Learning Safety

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      Firm sale: non returnable item
      SKU 9789811968136 Categories ,
      Select Guide Rating
      Machine learning algorithms allow computers to learn without being explicitly programmed. 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 ...

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      Description

      Product ID:9789811968136
      Product Form:Hardback
      Country of Manufacture:GB
      Series:Artificial Intelligence: Foundations, Theory, and Algorithms
      Title:Machine Learning Safety
      Authors:Author: Gaojie Jin, Xiaowei Huang, Wenjie Ruan
      Page Count:321
      Subjects:Computer security, Computer security, Machine learning, Machine learning
      Description:Select Guide Rating
      Machine learning algorithms allow computers to learn without being explicitly programmed. 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.
      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.


      Imprint Name:Springer Verlag, Singapore
      Publisher Name:Springer Verlag, Singapore
      Country of Publication:GB
      Publishing Date:2023-04-29

      Additional information

      Weight682 g
      Dimensions161 × 242 × 26 mm