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Advances in Domain Adaptation Theory

Advances in Domain Adaptation Theory
  • Author : Ievgen Redko
  • Publsiher : Elsevier
  • Release : 23 August 2019
  • ISBN : 0081023472
  • Pages : 208 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKAdvances in Domain Adaptation Theory

Summary:
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 BOOKAdvances 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.


Dataset Shift in Machine Learning

Dataset Shift in Machine Learning
  • Author : Joaquin Quiñonero-Candela,Masashi Sugiyama,Neil D. Lawrence,Anton Schwaighofer
  • Publisher : Mit Press
  • Release : 06 March 2021
GET THIS BOOKDataset Shift in Machine Learning

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


Advances in Neural Information Processing Systems 19

Advances in Neural Information Processing Systems 19
  • Author : Bernhard Schölkopf,John Platt,Thomas Hofmann
  • Publisher : MIT Press
  • Release : 06 March 2021
GET THIS BOOKAdvances in Neural Information Processing Systems 19

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.


ECAI 2020

ECAI 2020
  • Author : G. De Giacomo,A. Catala,B. Dilkina
  • Publisher : IOS Press
  • Release : 11 September 2020
GET THIS BOOKECAI 2020

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.


Advances in Machine Learning

Advances in Machine Learning
  • Author : Zhi-Hua Zhou,Takashi Washio
  • Publisher : Springer
  • Release : 03 November 2009
GET THIS BOOKAdvances in Machine Learning

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


Domain Adaptation in Computer Vision Applications

Domain Adaptation in Computer Vision Applications
  • Author : Gabriela Csurka
  • Publisher : Anonim
  • Release : 28 June 2018
GET THIS BOOKDomain Adaptation in Computer Vision Applications

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



Urban Sustainability in Theory and Practice

Urban Sustainability in Theory and Practice
  • Author : Paul James
  • Publisher : Routledge
  • Release : 19 September 2014
GET THIS BOOKUrban Sustainability in Theory and Practice

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


Artificial Intelligence

Artificial Intelligence
  • Author : Marco Antonio Aceves-Fernandez
  • Publisher : BoD – Books on Demand
  • Release : 27 June 2018
GET THIS BOOKArtificial Intelligence

Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in


Computer Vision – ECCV 2012

Computer Vision – ECCV 2012
  • Author : Andrew Fitzgibbon,Svetlana Lazebnik,Pietro Perona,Yoichi Sato,Cordelia Schmid
  • Publisher : Springer
  • Release : 26 September 2012
GET THIS BOOKComputer Vision – ECCV 2012

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


The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory
  • Author : Vladimir N. Vapnik
  • Publisher : Springer Science & Business Media
  • Release : 17 April 2013
GET THIS BOOKThe Nature of Statistical Learning Theory

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from


Recent Advances in Big Data and Deep Learning

Recent Advances in Big Data and Deep Learning
  • Author : Luca Oneto,Nicolò Navarin,Alessandro Sperduti,Davide Anguita
  • Publisher : Springer
  • Release : 02 April 2019
GET THIS BOOKRecent Advances in Big Data and Deep Learning

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


Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
  • Author : Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
  • Publisher : Springer
  • Release : 29 December 2017
GET THIS BOOKMachine Learning and Knowledge Discovery in Databases

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;


Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling
  • Author : Danilo Comminiello,Jose C. Principe
  • Publisher : Butterworth-Heinemann
  • Release : 11 June 2018
GET THIS BOOKAdaptive Learning Methods for Nonlinear System Modeling

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


Handbook of Self-regulation

Handbook of Self-regulation
  • Author : Monique Boekaerts,Paul R. Pintrich,Moshe Zeidner
  • Publisher : Elsevier
  • Release : 06 March 2021
GET THIS BOOKHandbook of Self-regulation

The Handbook of Self-Regulation represents state-of-the-art coverage of the latest theory, research, and developments in applications of self-regulation research. Chapters are of interest to psychologists interested in the development and operation of self-regulation as well as applications to health, organizational, clinical, and educational psychology.This book pulls together theory, research, and applications in the self-regulation domain and provides broad coverage of conceptual, methodological, and treatment issues. In view of the burgeoning interest and massive research on various aspects of self-regulation,