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Advances In Domain Adaptation Theory

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Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory Book
Author : Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani
Publisher : Elsevier
Release : 2019-08-23
ISBN : 0081023472
Language : En, Es, Fr & De

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Book Description :

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

Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods

Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods Book
Author : Ryan Kuo-Lung Lian,Ramadhani Kurniawan Subroto,Victor Andrean,Bing Hao Lin
Publisher : John Wiley & Sons
Release : 2021-11-01
ISBN : 1119527155
Language : En, Es, Fr & De

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Book Description :

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 harmonic analysis and modeling of power converter systems. The book covers a variety of power converters including DC-DC converters, diode bridge rectifiers (AC-DC), and voltage source converters (DC-AC). The authors provide in-depth guidance on modeling and simulating power converter systems. Detailed chapters contain relevant theory, practical examples, clear illustrations, sample Python and MATLAB codes, and validation enabling readers to build their own harmonic models for various PEDs and integrate them with existing power flow programs such as OpenDss. This book: Presents comprehensive large-signal and small-signal harmonic modeling of voltage source converters with various topologies Describes how to use accurate steady-state models of PEDs to predict how device harmonics will interact with the rest of the power system Explains the definitions of harmonics, power quality indices, and steady-state analysis of power systems Covers generalized steady-state modeling techniques, and accelerated methods for closed-loop converters Shows how the presented models can be combined with neural networks for power system parameter estimations Harmonic Modeling of Voltage Source Converters using Basic Numerical Methods is an indispensable reference and guide for researchers and graduate students involved in power quality and harmonic analysis, power engineers working in the field of harmonic power flow, developers of power simulation software, and academics and power industry professionals wanting to learn about harmonic modeling on power converters.

Advances in Neural Computation Machine Learning and Cognitive Research V

Advances in Neural Computation  Machine Learning  and Cognitive Research V Book
Author : Boris Kryzhanovsky
Publisher : Springer Nature
Release : 2021-12-07
ISBN : 3030915816
Language : En, Es, Fr & De

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Book Description :

Download Advances in Neural Computation Machine Learning and Cognitive Research V book written by Boris Kryzhanovsky, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Advances in Neural Information Processing Systems 19

Advances in Neural Information Processing Systems 19 Book
Author : Bernhard Schölkopf,John Platt,Thomas Hofmann
Publisher : MIT Press
Release : 2007
ISBN : 0262195682
Language : En, Es, Fr & De

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Book Description :

The annual conference on NIPS is the flagship conference on neural computation. It draws top academic researchers from around the world & is considered to be a showcase conference for new developments in network algorithms & architectures. This volume contains all of the papers presented at NIPS 2006.

Runtime Verification

Runtime Verification Book
Author : Lu Feng,Dana Fisman
Publisher : Springer Nature
Release : 2021-10-05
ISBN : 3030884945
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publisher : John Wiley & Sons
Release : 2021-08-18
ISBN : 1119646162
Language : En, Es, Fr & De

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Book Description :

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 treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining Book
Author : Kamal Karlapalem,Hong Cheng,Naren Ramakrishnan,R. K. Agrawal,P. Krishna Reddy,Jaideep Srivastava,Tanmoy Chakraborty
Publisher : Springer Nature
Release : 2021-05-08
ISBN : 3030757625
Language : En, Es, Fr & De

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Book Description :

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Frank Hutter,Tijl De Bie,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Release : 2021
ISBN : 3030676587
Language : En, Es, Fr & De

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Book Description :

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 follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. .

Advances in Data Mining Applications and Theoretical Aspects

Advances in Data Mining  Applications and Theoretical Aspects Book
Author : Petra Perner
Publisher : Springer
Release : 2018-07-04
ISBN : 3319957864
Language : En, Es, Fr & De

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Book Description :

This volume constitutes the proceedings of the 18th Industrial Conference on Adances in Data Mining, ICDM 2018, held in New York, NY, USA, in July 2018. The 24 regular papers presented in this book were carefully reviewed and selected from 146 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture, and in process control, industry, and society.

Advances in Intelligent Data Analysis XVIII

Advances in Intelligent Data Analysis XVIII Book
Author : Michael R. Berthold,Ad Feelders,Georg Krempl
Publisher : Springer Nature
Release : 2020-04-22
ISBN : 3030445844
Language : En, Es, Fr & De

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Book Description :

This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.

Riemannian Computing in Computer Vision

Riemannian Computing in Computer Vision Book
Author : Pavan K. Turaga,Anuj Srivastava
Publisher : Springer
Release : 2015-11-09
ISBN : 3319229575
Language : En, Es, Fr & De

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Book Description :

This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours).

Advances in Data Science and Information Engineering

Advances in Data Science and Information Engineering Book
Author : Robert Stahlbock,Gary M. Weiss,Mahmoud Abou-Nasr,Cheng-Ying Yang,Hamid R. Arabnia,Leonidas Deligiannidis
Publisher : Springer Nature
Release : 2021-10-29
ISBN : 3030717046
Language : En, Es, Fr & De

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Book Description :

The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students.

Computer Vision ECCV 2020

Computer Vision     ECCV 2020 Book
Author : Andrea Vedaldi,Horst Bischof,Thomas Brox,Jan-Michael Frahm
Publisher : Springer Nature
Release : 2020-10-29
ISBN : 3030585484
Language : En, Es, Fr & De

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Book Description :

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; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Vision based Pedestrian Protection Systems for Intelligent Vehicles

Vision based Pedestrian Protection Systems for Intelligent Vehicles Book
Author : David Gerónimo,Antonio M. López
Publisher : Springer Science & Business Media
Release : 2013-08-31
ISBN : 1461479878
Language : En, Es, Fr & De

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Book Description :

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 this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented.

Advances in Systems Science

Advances in Systems Science Book
Author : Jerzy Świątek,Jakub M. Tomczak
Publisher : Springer
Release : 2016-11-04
ISBN : 3319489445
Language : En, Es, Fr & De

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Book Description :

This book gathers the carefully reviewed proceedings of the 19th International Conference on Systems Science, presenting recent research findings in the areas of Artificial Intelligence, Machine Learning, Communication/Networking and Information Technology, Control Theory, Decision Support, Image Processing and Computer Vision, Optimization Techniques, Pattern Recognition, Robotics, Service Science, Web-based Services, Uncertain Systems and Transportation Systems. The International Conference on Systems Science was held in Wroclaw, Poland from September 7 to 9, 2016, and addressed a range of topics, including systems theory, control theory, machine learning, artificial intelligence, signal processing, communication and information technologies, transportation systems, multi-robotic systems and uncertain systems, as well as their applications. The aim of the conference is to provide a platform for communication between young and established researchers and practitioners, fostering future joint research in systems science.

Domain Adaptation and Representation Transfer and Distributed and Collaborative Learning

Domain Adaptation and Representation Transfer  and Distributed and Collaborative Learning Book
Author : Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu
Publisher : Springer Nature
Release : 2020-09-25
ISBN : 3030605485
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.

Domain Adaptation in Computer Vision with Deep Learning

Domain Adaptation in Computer Vision with Deep Learning Book
Author : Hemanth Venkateswara,Sethuraman Panchanathan
Publisher : Springer Nature
Release : 2020-08-18
ISBN : 3030455297
Language : En, Es, Fr & De

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Book Description :

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

ECAI 2020

ECAI 2020 Book
Author : G. De Giacomo,A. Catala,B. Dilkina
Publisher : IOS Press
Release : 2020-09-11
ISBN : 164368101X
Language : En, Es, Fr & De

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Book Description :

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. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.

Smart Multimedia

Smart Multimedia Book
Author : Troy McDaniel,Stefano Berretti,Igor D. D. Curcio,Anup Basu
Publisher : Springer Nature
Release : 2020-07-31
ISBN : 3030544079
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the Second International Conference on Smart Multimedia, ICSM 2019, which was held in San Diego, CA, USA, in December 2019. The 45 papers presented were selected from about 100 submissions and are grouped in sections on 3D mesh and depth image processing; image understanding; miscellaneous; smart multimedia for citizen-centered smart living; 3D perception and applications; video applications; multimedia in medicine; haptics and applications; smart multimedia beyond the visible spectrum; machine learning for multimedia; image segmentation and processing; biometrics; 3D and image processing; and smart social and connected household products.

Data Classification

Data Classification Book
Author : Charu C. Aggarwal
Publisher : CRC Press
Release : 2014-07-25
ISBN : 1498760589
Language : En, Es, Fr & De

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Book Description :

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi