Kód: 38809270
Existing adversarial learning algorithms differ in design assumptions regarding adversary's knowledge, attack strategies, attack influence, and security violation. In this book provides insights on the relation between adversarial ... celý popis
5490 Kč
Potřebujete více kusů?Máte-li zájem o více kusů, prověřte, prosím, nejprve dostupnost titulu na naši zákaznické podpoře.
Nákupem získáte 549 bodů
Existing adversarial learning algorithms differ in design assumptions regarding adversary's knowledge, attack strategies, attack influence, and security violation. In this book provides insights on the relation between adversarial learning and cybersecurity. The authors survey and summarize non-stationary data representations learnt by deep learning networks in big data, evolutionary computing, fog computing, cyber-physical systems, transfer learning, sparse learning, robust learning, and reinforcement learning. The robustness of deep learning networks is examined to produce a taxonomy of adversarial examples and algorithms. The authors also survey the use of game theory, convex optimization and stochastic optimization in adversarial deep learning formulations.
Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence
5490 Kč
Osobní odběr Praha, Brno a 12903 dalších
Copyright ©2008-24 nejlevnejsi-knihy.cz Všechna práva vyhrazenaSoukromíCookies
Nákupní košík ( prázdný )