huang xiaowei (55 resultados)

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Taschenbuch. Condición: 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 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.

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Condición: New. 2023rd edition NO-PA16APR2015-KAP.

Hardback. Condición: New. 2023 ed. 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.

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Taschenbuch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.

Buch. Condición: 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 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.

Condición: New.

Hardback. Condición: New. 2023 ed. 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.

Gebunden. Condición: New. Provides a comprehensive and thorough investigation on safety concerns regarding machine learningShows readers to identify vulnerabilities in machine learning models and to improve the models in the training processDemonstrates formal verif.

Buch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.

Hardcover. Condición: Brand New. 338 pages. 9.25x6.10x0.81 inches. In Stock.

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Paperback. Condición: Brand New. 235 pages. 9.25x6.10x9.25 inches. In Stock.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book dives into the vanguard of robotics and AI with this scholarly edition, a compilation of pioneering research from the 11th International Conference on Robot Intelligence Technology and Applications (RiTA) Volume 2. This book focuses on 'Machine Learning and AI Applications', which elucidates the transformative power of contemporary AI across a multitude of domains, from medical diagnostics to natural language comprehension. This book uncovers innovative models, avant-garde training techniques and sophisticated interpretability methods that underscore the versatility and robustness of AI. This book is an indispensable asset for researchers and practitioners navigating the digital frontier.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book delves into the forefront of robotics and AI with this scholarly collection from the 11th International Conference on Robot Intelligence Technology and Applications (RiTA) Volume 1. This book consists of two Sections/Chapters, namely Sensors, Signals and Systems and Robotics, Automation and Control. The former elucidates the pivotal role of sensors, signals and systems in propelling advancements in robotics and AI, while the latter illuminates the transformative power of autonomous systems across various sectors, from intricate surgical procedures to complex manufacturing processes. This book is an indispensable resource for both researchers and practitioners, offering a comprehensive overview of the digital trajectory of robotics and AI.

Condición: New.

Taschenbuch. Condición: Neu. Neuware -This book delves into the forefront of robotics and AI with this scholarly collection from the 11th International Conference on Robot Intelligence Technology and Applications (RiTA) Volume 1. This book consists of two Sections/Chapters, namely Sensors, Signals and Systems and Robotics, Automation and Control. The former elucidates the pivotal role of sensors, signals and systems in propelling advancements in robotics and AI, while the latter illuminates the transformative power of autonomous systems across various sectors, from intricate surgical procedures to complex manufacturing processes. This book is an indispensable resource for both researchers and practitioners, offering a comprehensive overview of the digital trajectory of robotics and AI.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch.

Taschenbuch. Condición: Neu. Neuware -This book dives into the vanguard of robotics and AI with this scholarly edition, a compilation of pioneering research from the 11th International Conference on Robot Intelligence Technology and Applications (RiTA) Volume 2. This book focuses on ¿Machine Learning and AI Applications¿, which elucidates the transformative power of contemporary AI across a multitude of domains, from medical diagnostics to natural language comprehension. This book uncovers innovative models, avant-garde training techniques and sophisticated interpretability methods that underscore the versatility and robustness of AI. This book is an indispensable asset for researchers and practitioners navigating the digital frontier.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 236 pp. Englisch.