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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

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 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 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.

Dataset Shift in Machine Learning

Dataset Shift in Machine Learning
A Book

by Joaquin Quiñonero-Candela,Masashi Sugiyama,Neil D. Lawrence,Anton Schwaighofer

  • Publisher : Neural Information Processing
  • Release : 2009
  • Pages : 229
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama

Domain Adaptation in Computer Vision Applications

Domain Adaptation in Computer Vision Applications
A Book

by Gabriela Csurka

  • Publisher : Unknown Publisher
  • Release : 2018-06-28
  • Pages : 329
  • ISBN : 9783319863832
  • Language : En, Es, Fr & De
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This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes. Topics and features: Surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures Presents a positioning of the dataset bias in the CNN-based feature arena Proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data Discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models Addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection Describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning. Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Xerox Research Centre Europe, Meylan, France.

Knowledge Discovery in Databases: PKDD 2007

Knowledge Discovery in Databases: PKDD 2007
11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings

by Joost N. Kok,Jacek Koronacki,Ramon Lopez de Mantaras,Stan Matwin,Dunja Mladenic

  • Publisher : Springer
  • Release : 2007-08-30
  • Pages : 644
  • ISBN : 3540749764
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, held in Warsaw, Poland, co-located with ECML 2007, the 18th European Conference on Machine Learning. The 28 revised full papers and 35 revised short papers present original results on leading-edge subjects of knowledge discovery from conventional and complex data and address all current issues in the area.

Transfer Learning

Transfer Learning
A Book

by Qiang Yang,Yu Zhang,Wenyuan Dai,Sinno Jialin Pan

  • Publisher : Cambridge University Press
  • Release : 2020-01-31
  • Pages : 393
  • ISBN : 1107016908
  • Language : En, Es, Fr & De
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This in-depth tutorial for students, researchers, and developers covers foundations, plus applications ranging from search to multimedia.

Advances in Archaeological Method and Theory

Advances in Archaeological Method and Theory
A Book

by Anonim

  • Publisher : Elsevier
  • Release : 2014-06-28
  • Pages : 455
  • ISBN : 1483294285
  • Language : En, Es, Fr & De
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Advances in Archaeological Method and Theory

Recent Advances in Big Data and Deep Learning

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

by Luca Oneto,Nicolò Navarin,Alessandro Sperduti,Davide Anguita

  • Publisher : Springer
  • Release : 2019-04-02
  • Pages : 392
  • ISBN : 3030168417
  • Language : En, Es, Fr & De
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This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.

Adaptation in Natural and Artificial Systems

Adaptation in Natural and Artificial Systems
An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence

by John Henry Holland,Professor of Psychology and of Electrical Engineering and Computer Science John H Holland,Senior Lecturer in Human Resource Management Holland

  • Publisher : MIT Press
  • Release : 1992
  • Pages : 211
  • ISBN : 9780262581110
  • Language : En, Es, Fr & De
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List of figures. Preface to the 1992 edition. Preface. The general setting. A formal framework. lustrations. Schemata. The optimal allocation of trials. Reproductive plans and genetic operators. The robustness of genetic plans. Adaptation of codings and representations. An overview. Interim and prospectus. Glossary of important symbols.

Computer Vision – ECCV 2012

Computer Vision – ECCV 2012
12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings

by Andrew Fitzgibbon,Svetlana Lazebnik,Pietro Perona,Yoichi Sato,Cordelia Schmid

  • Publisher : Springer
  • Release : 2012-09-26
  • Pages : 889
  • ISBN : 3642337090
  • Language : En, Es, Fr & De
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The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

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 Science & Business Media
  • Release : 2009-10-06
  • Pages : 413
  • ISBN : 3642052231
  • 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.

Person Re-Identification

Person Re-Identification
A Book

by Shaogang Gong,Marco Cristani,Shuicheng Yan,Chen Change Loy

  • Publisher : Springer Science & Business Media
  • Release : 2014-01-03
  • Pages : 445
  • ISBN : 144716296X
  • Language : En, Es, Fr & De
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The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.

Advances in Connectionist and Neural Computation Theory

Advances in Connectionist and Neural Computation Theory
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1994
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings

by Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski

  • Publisher : Springer
  • Release : 2017-12-29
  • Pages : 866
  • ISBN : 3319712462
  • Language : En, Es, Fr & De
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The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling
A Book

by Danilo Comminiello,Jose C. Principe

  • Publisher : Butterworth-Heinemann
  • Release : 2018-06-11
  • Pages : 388
  • ISBN : 0128129778
  • Language : En, Es, Fr & De
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Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.

Urban Sustainability in Theory and Practice

Urban Sustainability in Theory and Practice
Circles of sustainability

by Paul James

  • Publisher : Routledge
  • Release : 2014-09-19
  • Pages : 260
  • ISBN : 1317658353
  • Language : En, Es, Fr & De
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Cities are home to the most consequential current attempts at human adaptation and they provide one possible focus for the flourishing of life on this planet. However, for this to be realized in more than an ad hoc way, a substantial rethinking of current approaches and practices needs to occur. Urban Sustainability in Theory and Practice responds to the crises of sustainability in the world today by going back to basics. It makes four major contributions to thinking about and acting upon cities. It provides a means of reflexivity learning about urban sustainability in the process of working practically for positive social development and projected change. It challenges the usually taken-for-granted nature of sustainability practices while providing tools for modifying those practices. It emphasizes the necessity of a holistic and integrated understanding of urban life. Finally it rewrites existing dominant understandings of the social whole such as the triple-bottom line approach that reduce environmental questions to externalities and social questions to background issues. The book is a much-needed practical and conceptual guide for rethinking urban engagement. Covering the full range of sustainability domains and bridging discourses aimed at academics and practitioners, this is an essential read for all those studying, researching and working in urban geography, sustainability assessment, urban planning, urban sociology and politics, sustainable development and environmental studies.

Advances in Information Retrieval

Advances in Information Retrieval
31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009, Proceedings

by Mohand Boughanem,Catherine Berrut,Josiane Mothe,Chantal Soule-Dupuy

  • Publisher : Springer Science & Business Media
  • Release : 2009-03-27
  • Pages : 821
  • ISBN : 3642009573
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 30th annual European Conference on Information Retrieval Research, ECIR 2009, held in Toulouse, France in April 2009. The 42 revised full papers and 18 revised short papers presented together with the abstracts of 3 invited lectures and 25 poster papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections on retrieval model, collaborative IR / filtering, learning, multimedia - metadata, expert search - advertising, evaluation, opinion detection, web IR, representation, clustering / categorization as well as distributed IR.

Algorithmic Advances in Riemannian Geometry and Applications

Algorithmic Advances in Riemannian Geometry and Applications
For Machine Learning, Computer Vision, Statistics, and Optimization

by Hà Quang Minh,Vittorio Murino

  • Publisher : Springer
  • Release : 2016-10-05
  • Pages : 208
  • ISBN : 3319450263
  • Language : En, Es, Fr & De
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This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.