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Information Processing Letters, ISSN 0020-0190, 2010, Volume 111, Issue 1, pp. 40 - 45
In this paper, we study the quantum PAC learning model, offering an improved lower bound on the query complexity. For a concept class with VC dimension d, the... 
PAC (Probably Approximately Correct) learning | Lower bound | Quantum algorithm | VC dimension | Computational complexity | COMPUTER SCIENCE, INFORMATION SYSTEMS | learning | PAC (Probably Approximately Correct) | Analysis | Algorithms | Learning | Lower bounds | Accuracy | Query processing | Confidence | Images | Complexity
Journal Article
IEEE Transactions on Communications, ISSN 0090-6778, 09/2018, Volume 66, Issue 9, pp. 3837 - 3847
Journal Article
IEEE Transactions on Multi-Scale Computing Systems, ISSN 2332-7766, 07/2016, Volume 2, Issue 3, pp. 146 - 159
We present a lightweight PUF-based authentication approach that is practical in settings where a server authenticates a device, and for use cases where the... 
computationally unrestricted adversary | probably approximately correct (PAC) learning | Protocols | Authentication | Physical unclonable function | heuristic security | Silicon | Manufacturing | Servers | Cryptography | machine learning | authentication
Journal Article
Journal of Computer and System Sciences, ISSN 0022-0000, 2005, Volume 70, Issue 4, pp. 471 - 484
Journal Article
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, ISSN 1003-6059, 10/2014, Volume 27, Issue 10, pp. 865 - 872
Journal Article
Journal of Computer and System Sciences, ISSN 0022-0000, 2004, Volume 68, Issue 3, pp. 521 - 545
Journal Article
Machine Learning, ISSN 0885-6125, 5/2002, Volume 47, Issue 2, pp. 133 - 151
Journal Article
Book Chapter
One of the main criticisms of previously studied label noise models in the PAC-learning framework is the inability of such models to represent the noise in... 
PAC learning, label noise, minimum disagreement strategy, binary classification | noise, class noise, PAC, probably approximately correct
Dissertation
2010, Lecture Notes in Computer Science, ISBN 9783642161070, Volume 6331
Book Chapter
2010, Lecture Notes in Computer Science, ISBN 9783642154874, Volume 6339
Book Chapter
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