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Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2025
ISBN 10: 3032001390 ISBN 13: 9783032001399
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Sprache: Englisch
Verlag: Springer, Springer Okt 2025, 2025
ISBN 10: 3032001390 ISBN 13: 9783032001399
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Taschenbuch. Zustand: Neu. Neuware - The two-volume set CCIS 2617 and 2618constitutes the refereed post-conferenceproceedings of the 5th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2025, held in Sesti Levante, Italy, during April 28 30, 2025.The 38 revised full papers presented in these proceedings were carefully reviewed and selected from 92 submissions. The papers are organized in the following topical sections:Part I: Optimization; Optimization in Control Systems Design; Artificial Intelligence in Healthcare and Medicine; and Deep Learning.Part II: Optimization in the SDG context; Machine Learning; and Machine Learning and Artificial Intelligence in Robotics.
Taschenbuch. Zustand: Neu. Optimization, Learning Algorithms and Applications | 5th International Conference, OL2A 2025, Sesti Levante, Italy, April 28-30, 2025, Proceedings, Part II | Ana I. Pereira (u. a.) | Taschenbuch | Communications in Computer and Information Science | xv | Englisch | 2025 | Springer | EAN 9783032001399 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Verlag: Springer-Nature New York Inc, 2025
ISBN 10: 3032001366 ISBN 13: 9783032001368
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Taschenbuch. Zustand: Neu. Optimization, Learning Algorithms and Applications | 5th International Conference, OL2A 2025, Sesti Levante, Italy, April 28-30, 2025, Proceedings, Part I | Ana I. Pereira (u. a.) | Taschenbuch | Communications in Computer and Information Science | xvi | Englisch | 2025 | Springer | EAN 9783032001368 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The two-volume set CCIS 2617 and 2618constitutes the refereed post-conferenceproceedings of the 5th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2025, held in Sesti Levante, Italy, during April 28 30, 2025.The 38 revised full papers presented in these proceedings were carefully reviewed and selected from 92 submissions. The papers are organized in the following topical sections:Part I: Optimization; Optimization in Control Systems Design; Artificial Intelligence in Healthcare and Medicine; and Deep Learning.Part II: Optimization in the SDG context; Machine Learning; and Machine Learning and Artificial Intelligence in Robotics.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Taschenbuch. Zustand: Neu. Model Selection and Error Estimation in a Nutshell | Luca Oneto | Taschenbuch | Modeling and Optimization in Science and Technologies | xiii | Englisch | 2020 | Springer | EAN 9783030243616 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2020
ISBN 10: 3030243613 ISBN 13: 9783030243616
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80's and includes the most recent results. It discusses open problems and outlines future directions for research.
Sprache: Englisch
Verlag: Springer International Publishing, 2019
ISBN 10: 3030243583 ISBN 13: 9783030243586
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80's and includes the most recent results. It discusses open problems and outlines future directions for research.
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EUR 162,76
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Taschenbuch. Zustand: Neu. Recent Trends in Learning From Data | Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) | Luca Oneto (u. a.) | Taschenbuch | vii | Englisch | 2021 | Springer | EAN 9783030438852 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Taschenbuch. Zustand: Neu. Recent Advances in Big Data and Deep Learning | Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019 | Luca Oneto (u. a.) | Taschenbuch | x | Englisch | 2019 | Springer | EAN 9783030168407 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2021
ISBN 10: 3030438856 ISBN 13: 9783030438852
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.
Sprache: Englisch
Verlag: Springer International Publishing, 2020
ISBN 10: 3030438821 ISBN 13: 9783030438821
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.