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	Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 494 Reviews about "Learning (I.2.6)": Date Reviewed    Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks Ghanem M., Chen T., Nepomuceno E. Journal of Intelligent Information Systems 601-23, 2023.  Type: Article Cybersecurity practitioners require alternative approaches and tools for combating the emerging security threats worldwide. How effective are current penetration testing (PT) training modules in cybersecurity degree and certification programs for ... Dec 18 2023   Detecting deception using machine learning with facial expressions and pulse rate Tsuchiya K., Hatano R., Nishiyama H. Artificial Life and Robotics 28(3): 509-519, 2023.  Type: Article This is a very important paper because it combines machine learning, facial expressions analysis, and pulse rate in order to detect deception in an interview context. The results are supported by strong machine learning validation settings, applyi... Dec 8 2023   Reinforcement learning for quantitative trading Sun S., Wang R., An B. ACM Transactions on Intelligent Systems and Technology 12(2): 2023.  Type: Article This paper is a good starting point for both learners and practitioners in the fields of algorithmic trading, portfolio management, order execution, market making, and even reinforcement learning (RL), as they launch their deep dives into the inte... Aug 10 2023   A survey of inverse reinforcement learning Adams S., Cody T., Beling P. Artificial Intelligence Review 55(6): 4307-4346, 2022.  Type: Article The process of discovering and incorporating knowledge from educators into machine learning (ML) poses major challenges. Beyond coding the known or derived characteristics of systems into ML algorithms, how should innovative researchers discover, ... May 1 2023   Declarative machine learning systems Molino P., Molino P., Ré C., Ré C. Communications of the ACM 65(1): 42-49, 2021.  Type: Article This article presents future directions for how machine learning (ML) can be easily adopted across applications to take advantage of artificial intelligence (AI). In the past two decades, ML has been improving at a tremendous pace thanks to many c... Jan 3 2023   Machine learning: the basics Jung A., Springer International Publishing, Cham, Switzerland, 2022. 229 pp.  Type: Book (978-9-811681-92-9) As the old saying goes, “don’t judge a book by its cover.” This review will affirm that statement. The book covers the many aspects of machine learning, but it does so in a very terse manner. This is a large area of research and ... Dec 19 2022   Rule-based machine learning classification and knowledge discovery for complex problems Urbanowicz R. ACM SIGEVOlution 7(2-3): 3-11, 2020.  Type: Article The intricate spiraling problems in areas such as aeronautics, bioinformatics, and meteorology require innovative algorithms for intellectual discovery from unique data types and sources. How should “Space Age” intelligent-based system... Dec 8 2022   Hyperparameter optimization in machine learning Agrawal T., Apress, New York, NY, 2020. 188 pp.  Type: Book (978-1-484265-78-9) The book explores a variety of optimization algorithms, ranging from brute-force ones like grid search and random search and their distribution, to more complex ones like the Hyperband algorithm. Bayesian optimization, which has the ability to lea... Dec 2 2022   Cause Effect Pairs in Machine Learning Guyon I., Statnikov A., Bakir Batu B., Springer, New York, NY, 2020. 372 pp.  Type: Book (978-3-030218-09-6) From the earliest courses in statistics we are taught that correlation does not imply causation. However, apart from some special cases, it is also generally true that causation implies correlation (or dependency). While the first sent... May 17 2022   A survey of machine learning for big code and naturalness Allamanis M., Barr E., Devanbu P., Sutton C. ACM Computing Surveys 51(4): 1-37, 2018.  Type: Article There is a rising demand for effective software tools that can help developers build reliable and maintainable software systems. There has been abundant research to help developers track bugs and verify program properties and refactor ... Nov 4 2021       Display 510152550100 per page
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