Advances in Domain Adaptation Theory

Produk Detail:
  • Author : Ievgen Redko
  • Publisher : Elsevier
  • Pages : 208 pages
  • ISBN : 0081023472
  • Rating : /5 from reviews
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Download or Read online Advances in Domain Adaptation Theory full in PDF, ePub and kindle. this book written by Ievgen Redko and published by Elsevier which was released on 23 August 2019 with total page 208 pages. We cannot guarantee that Advances in Domain Adaptation Theory book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version. Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm. Gives an overview of current results on transfer learning Focuses on the adaptation of the field from a theoretical point-of-view Describes four major families of theoretical results in the literature Summarizes existing results on adaptation in the field Provides tips for future research

Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory
  • Author : Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani
  • Publisher : Elsevier
  • Release : 23 August 2019
GET THIS BOOK Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds.

Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods

Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods
  • Author : Ryan Kuo-Lung Lian,Ramadhani Kurniawan Subroto,Victor Andrean,Bing Hao Lin
  • Publisher : John Wiley & Sons
  • Release : 01 November 2021
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Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods One of the first books to bridge the gap between frequency domain and time-domain methods of steady-state modeling of power electronic converters Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods presents detailed coverage of steady-state modeling of power electronic devices (PEDs). This authoritative resource describes both large-signal and small-signal modeling of power converters and how some of the simple and commonly used numerical methods can be applied for

Runtime Verification

Runtime Verification
  • Author : Lu Feng,Dana Fisman
  • Publisher : Springer Nature
  • Release : 05 October 2021
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This book constitutes the refereed proceedings of the 21st International Conference on Runtime Verification, RV 2021, held virtually during October 11-14, 2021. The 11 regular papers and 7 short/tool/benchmark papers presented in this book were carefully reviewed and selected from 40 submissions. Also included is one tutorial paper. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions.

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences
  • Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
  • Publisher : John Wiley & Sons
  • Release : 18 August 2021
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DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and

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Machine Learning and Knowledge Discovery in Databases
  • Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
  • Publisher : Springer Nature
  • Release : 24 February 2021
GET THIS BOOK Machine Learning and Knowledge Discovery in Databases

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as

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Computer Vision     ECCV 2020
  • Author : Andrea Vedaldi,Horst Bischof,Thomas Brox,Jan-Michael Frahm
  • Publisher : Springer Nature
  • Release : 29 October 2020
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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. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification;

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Vision based Pedestrian Protection Systems for Intelligent Vehicles
  • Author : David Gerónimo,Antonio M. López
  • Publisher : Springer Science & Business Media
  • Release : 31 August 2013
GET THIS BOOK Vision based Pedestrian Protection Systems for Intelligent Vehicles

Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform

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Advanced Methods and Deep Learning in Computer Vision
  • Author : E. R. Davies,Matthew Turk
  • Publisher : Academic Press
  • Release : 09 November 2021
GET THIS BOOK Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This

ECAI 2020

ECAI 2020
  • Author : G. De Giacomo,A. Catala,B. Dilkina
  • Publisher : IOS Press
  • Release : 11 September 2020
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This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology.

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Medical Image Computing and Computer Assisted Intervention     MICCAI 2021
  • Author : Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert
  • Publisher : Springer Nature
  • Release : 23 September 2021
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The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III:

Medical Image Computing and Computer Assisted Intervention MICCAI 2021

Medical Image Computing and Computer Assisted Intervention     MICCAI 2021
  • Author : Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert
  • Publisher : Springer Nature
  • Release : 22 September 2021
GET THIS BOOK Medical Image Computing and Computer Assisted Intervention MICCAI 2021

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III:

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Information Systems Development
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  • Publisher : Springer Science & Business Media
  • Release : 08 February 2006
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This volume is comprised of the proceedings of the 13th International Conference on Information Systems Development held August 26th-28th, 2004, at Vilnius Gediminas Technical University, Vilnius, Lithuania. The aim of this volume is to provide a forum for the research and practices addressing current issues associated with Information Systems Development (ISD). Every day, new technologies, applications, and methods raise the standards for the quality of systems expected by organizations as well as end users. All are becoming dependent on systems