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1. |
Record Nr. |
UNINA9910696557003321 |
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Titolo |
Traffic safety [[electronic resource] ] : grants generally address key safety issues, despite state eligibility and management difficulties : report to the Committee on Transportation and Infrastructure, House of Representatives |
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Pubbl/distr/stampa |
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[Washington, D.C.] : , : U.S. Govt. Accountability Office, , [2008] |
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Descrizione fisica |
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iii, 55 pages : digital, PDF file |
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Soggetti |
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Traffic safety - United States - States - Finance |
Traffic fatalities - United States - States - Prevention |
Grants-in-aid - United States |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from title screen (viewed on Apr. 17, 2008). |
"March 2008." |
Paper version available from: U.S. Govt. Accountability Office, 441 G St., NW, Rm. LM, Washington, D.C. 20548. |
"GAO-08-398." |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNISA996542664103316 |
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Titolo |
Geometric science of information : 6th international conference, GSI 2023, St. Malo, France, August 30 - September 1, 2023, proceedings, Part II / / Frank Nielsen and Frédéric Barbaresco, editors |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
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©2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (669 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 14072 |
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Disciplina |
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Soggetti |
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Computer science - Mathematics |
Information science |
Geometry - Data processing |
Artificial intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Geometry and machine learning -- Divergences and computational information geometry -- Statistics, topology and shape spaces -- Geometry and mechanics -- Geometry, learning dynamics and thermodynamics -- Quantum information geometry -- Geometry and biological structures -- Geometry and applications. |
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Sommario/riassunto |
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This book constitutes the proceedings of the 6th International Conference on Geometric Science of Information, GSI 2023, held in St. Malo, France, during August 30-September 1, 2023. The 125 full papers presented in this volume were carefully reviewed and selected from 161 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: geometry and machine learning; divergences and computational information geometry; statistics, topology and shape spaces; geometry and mechanics; geometry, learning dynamics and thermodynamics; quantum information geometry; geometry and biological structures; geometry and applications. |
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3. |
Record Nr. |
UNINA9910743351903321 |
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Titolo |
Distributed Machine Learning and Gradient Optimization / / by Jiawei Jiang, Bin Cui, Ce Zhang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
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ISBN |
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981-16-3419-X |
981-16-3420-3 |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (179 pages) |
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Collana |
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Big Data Management, , 2522-0187 |
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Disciplina |
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Soggetti |
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Machine learning |
Data mining |
Database management |
Machine Learning |
Data Mining and Knowledge Discovery |
Database Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. . |
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Sommario/riassunto |
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This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of |
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distributed machine learning. It will appeal toa broad audience in the field of machine learning, artificial intelligence, big data and database management. |
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