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AI-Based Transportation Planning and Operation

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AI-Based Transportation Planning and Operation

AI-Based Transportation Planning and Operation by Keemin Sohn
English | PDF | 2021 | 126 Pages | ISBN : 3036503641 | 13.5 MB

The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.

Machine Learning, Optimization, and Data Science

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Machine Learning, Optimization, and Data Science

Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I by Giuseppe Nicosia
English | EPUB | 2020 | 776 Pages | ISBN : 3030645827 | 54.6 MB

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020.

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

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Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability by Marcus Hutter
English | PDF | 2005 | 294 Pages | ISBN : 3540221395 | 32.7 MB

This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments.

Machine Learning, Optimization, and Data Science

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Machine Learning, Optimization, and Data Science

Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I by Giuseppe Nicosia
English | PDF | 2020 | 776 Pages | ISBN : 3030645827 | 64 MB

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020.

Computer Vision – ECCV 2020

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Computer Vision – ECCV 2020

Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX by Andrea Vedaldi
English | PDF | 2020 | 839 Pages | ISBN : 3030585646 | 140.2 MB

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic.

Computer Vision – ECCV 2020

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Computer Vision – ECCV 2020

Computer Vision – ECCV 2020: 6th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI by Andrea Vedaldi
English | PDF | 2020 | 832 Pages | ISBN : 3030585883 | 158. MB

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic.

Qualitative Spatial Abstraction in Reinforcement Learning (Repost)

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Qualitative Spatial Abstraction in Reinforcement Learning (Repost)

Qualitative Spatial Abstraction in Reinforcement Learning by Lutz Frommberger
English | PDF | 2010 | 186 Pages | ISBN : 3642165893 | 13.73 MB

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial.

Deep Reinforcement Learning for Wireless Networks

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Deep Reinforcement Learning for Wireless Networks

Deep Reinforcement Learning for Wireless Networks by F. Richard Yu
English | PDF(Reospt),EPUB | 2019 | 78 Pages | ISBN : 3030105458 | 10.3 MB

This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.

Genetic Algorithms for Machine Learning

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Genetic Algorithms for Machine Learning

Genetic Algorithms for Machine Learning by John J. Grefenstette
English | PDF | 1994 | 167 Pages | ISBN : 1461361826 | 19.34 MB

The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference.