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Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory
A Book

by Ievgen Redko,Emilie Morvant,Amaury Habrard,Marc Sebban,Younès Bennani

  • Publisher : Elsevier
  • Release : 2019-08-23
  • Pages : 208
  • ISBN : 0081023472
  • Language : En, Es, Fr & De
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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
A Book

by Ryan Kuo-Lung Lian,Ramadhani Kurniawan Subroto,Victor Andrean,Bing Hao Lin

  • Publisher : John Wiley & Sons
  • Release : 2021-11-01
  • Pages : 416
  • ISBN : 1119527155
  • Language : En, Es, Fr & De
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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 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 Information Processing Systems 19

Advances in Neural Information Processing Systems 19
Proceedings of the 2006 Conference

by Bernhard Schölkopf,John Platt,Thomas Hofmann

  • Publisher : MIT Press
  • Release : 2007
  • Pages : 1643
  • ISBN : 0262195682
  • Language : En, Es, Fr & De
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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.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part I

by João Gama

  • Publisher : Springer Nature
  • Release : 2022
  • Pages : 129
  • ISBN : 3031059336
  • Language : En, Es, Fr & De
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Runtime Verification

Runtime Verification
21st International Conference, RV 2021, Virtual Event, October 11–14, 2021, Proceedings

by Lu Feng,Dana Fisman

  • Publisher : Springer Nature
  • Release : 2021-10-05
  • Pages : 331
  • ISBN : 3030884945
  • Language : En, Es, Fr & De
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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
A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences

by Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein

  • Publisher : John Wiley & Sons
  • Release : 2021-08-18
  • Pages : 432
  • ISBN : 1119646162
  • Language : En, Es, Fr & De
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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 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
25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I

by Kamal Karlapalem,Hong Cheng,Naren Ramakrishnan,R. K. Agrawal,P. Krishna Reddy,Jaideep Srivastava,Tanmoy Chakraborty

  • Publisher : Springer Nature
  • Release : 2021-05-08
  • Pages : 834
  • ISBN : 3030757625
  • Language : En, Es, Fr & De
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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.

Advances in Machine Learning

Advances in Machine Learning
First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings

by Zhi-Hua Zhou,Takashi Washio

  • Publisher : Springer
  • Release : 2009-11-03
  • Pages : 413
  • ISBN : 364205224X
  • Language : En, Es, Fr & De
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The First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2–4, 2009.This was the ?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related ?elds to share their new ideas and research ?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews. Each submission was handled by an Area Chair who coordinated discussions among reviewers and made recommendation on the submission. The Program Committee Chairs examined the reviews and meta-reviews to further guarantee the reliability and integrity of the reviewing process. Twenty-nine - pers were selected after this process. To ensure that important revisions required by reviewers were incorporated into the ?nal accepted papers, and to allow submissions which would have - tential after a careful revision, this year we launched a “revision double-check” process. In short, the above-mentioned 29 papers were conditionally accepted, and the authors were requested to incorporate the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers’comments.Therevised?nal version and the revision list of each conditionally accepted paper was examined by the Area Chair and Program Committee Chairs. Papers that failed to pass the examination were ?nally rejected.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I

by Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera

  • Publisher : Springer Nature
  • Release : 2021-02-24
  • Pages : 764
  • ISBN : 3030676587
  • Language : En, Es, Fr & De
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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
18th Industrial Conference, ICDM 2018, New York, NY, USA, July 11-12, 2018, Proceedings

by Petra Perner

  • Publisher : Springer
  • Release : 2018-07-04
  • Pages : 326
  • ISBN : 3319957864
  • Language : En, Es, Fr & De
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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
18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings

by Michael R. Berthold,Ad Feelders,Georg Krempl

  • Publisher : Springer Nature
  • Release : 2020-04-22
  • Pages : 588
  • ISBN : 3030445844
  • Language : En, Es, Fr & De
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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.

Advances in Data Science and Information Engineering

Advances in Data Science and Information Engineering
Proceedings from ICDATA 2020 and IKE 2020

by Robert Stahlbock,Gary M. Weiss,Mahmoud Abou-Nasr,Cheng-Ying Yang,Hamid R. Arabnia,Leonidas Deligiannidis

  • Publisher : Springer Nature
  • Release : 2021-10-29
  • Pages : 986
  • ISBN : 3030717046
  • Language : En, Es, Fr & De
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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
16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IV

by Andrea Vedaldi,Horst Bischof,Thomas Brox,Jan-Michael Frahm

  • Publisher : Springer Nature
  • Release : 2020-10-29
  • Pages : 817
  • ISBN : 3030585484
  • Language : En, Es, Fr & De
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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; 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
A Book

by David Gerónimo,Antonio M. López

  • Publisher : Springer Science & Business Media
  • Release : 2013-08-31
  • Pages : 114
  • ISBN : 1461479878
  • Language : En, Es, Fr & De
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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.

AI*IA 2005: Advances in Artificial Intelligence

AI*IA 2005: Advances in Artificial Intelligence
9th Congress of the Italian Association for Artificial Intelligence Milan, Italy, September 21-23, 2005, Proceedings

by Associazione italiana per l'intelligenza artificiale. Congress,Associazione Italiana per l'Intelligenza Artificiale

  • Publisher : Springer Science & Business Media
  • Release : 2005-09-12
  • Pages : 614
  • ISBN : 3540290419
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 9th Congress of the Italian Association for Artificial Intelligence, AI*IA 2005, held in Milan, Italy in September 2005. The 46 revised full papers presented together with 16 revised short papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on either theoretical research with results and proposals, improvements and consolidations, or on applications as there are systems and prototypes, case studies and proposals. Within this classification some of the main classical topics of AI are presented (agents, knowledge representation, machine learning, planning, robotics, natural language, etc.), but here the focus is on the ability of AI computational approaches to face challenging problems and to propose innovative solutions.

Advances and Trends in Artificial Intelligence. From Theory to Practice

Advances and Trends in Artificial Intelligence. From Theory to Practice
32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings

by Franz Wotawa,Gerhard Friedrich,Ingo Pill,Roxane Koitz-Hristov,Moonis Ali

  • Publisher : Springer
  • Release : 2019-06-28
  • Pages : 865
  • ISBN : 3030229998
  • Language : En, Es, Fr & De
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This book constitutes the thoroughly refereed proceedings of the 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, held in Graz, Austria, in July 2019. The 41 full papers and 32 short papers presented were carefully reviewed and selected from 151 submissions. The IEA/AIE 2019 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include engineering, science, industry, automation and robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions. IEA/AIE 2019 will have a special focus on automated driving and autonomous systems and also contributions dealing with such systems or their verification and validation as well.

Advances in Systems Science

Advances in Systems Science
Proceedings of the International Conference on Systems Science 2016 (ICSS 2016)

by Jerzy Świątek,Jakub M. Tomczak

  • Publisher : Springer
  • Release : 2016-11-04
  • Pages : 340
  • ISBN : 3319489445
  • Language : En, Es, Fr & De
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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
Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

by 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
  • Pages : 212
  • ISBN : 3030605485
  • Language : En, Es, Fr & De
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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
A Book

by Hemanth Venkateswara,Sethuraman Panchanathan

  • Publisher : Springer Nature
  • Release : 2020-08-18
  • Pages : 256
  • ISBN : 3030455297
  • Language : En, Es, Fr & De
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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.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II

by Vincent S. Tseng,Tu Bao Ho,Zhi-Hua Zhou,Arbee L.P. Chen,Hung-Yu Kao

  • Publisher : Springer
  • Release : 2014-05-08
  • Pages : 624
  • ISBN : 3319066056
  • Language : En, Es, Fr & De
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The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, held in Tainan, Taiwan, in May 2014. The 40 full papers and the 60 short papers presented within these proceedings were carefully reviewed and selected from 371 submissions. They cover the general fields of pattern mining; social network and social media; classification; graph and network mining; applications; privacy preserving; recommendation; feature selection and reduction; machine learning; temporal and spatial data; novel algorithms; clustering; biomedical data mining; stream mining; outlier and anomaly detection; multi-sources mining; and unstructured data and text mining.