Principled Algorithms For Domain Adaptation And Generalization

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Advances in Domain Adaptation Theory

Author : Ievgen Redko
Publisher : Elsevier
ISBN 13 : 0081023472
Total Pages : 210 pages
Book Rating : 4.0/5 (81 download)

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Book Synopsis Advances in Domain Adaptation Theory by : Ievgen Redko

Download or read book Advances in Domain Adaptation Theory written by Ievgen Redko and published by Elsevier. This book was released on 2019-08-23 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Principles of Data Mining

Author : David J. Hand
Publisher : MIT Press
ISBN 13 : 9780262082907
Total Pages : 594 pages
Book Rating : 4.0/5 (829 download)

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Book Synopsis Principles of Data Mining by : David J. Hand

Download or read book Principles of Data Mining written by David J. Hand and published by MIT Press. This book was released on 2001-08-17 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Person Re-Identification

Author : Shaogang Gong
Publisher : Springer Science & Business Media
ISBN 13 : 144716296X
Total Pages : 446 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Person Re-Identification by : Shaogang Gong

Download or read book Person Re-Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Transfer Learning

Author : Qiang Yang
Publisher : Cambridge University Press
ISBN 13 : 1108860087
Total Pages : 394 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Transfer Learning by : Qiang Yang

Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Advances in Domain Adaptation Theory

Author : Ievgen Redko
Publisher : ISTE Press - Elsevier
ISBN 13 : 178548236X
Total Pages : 208 pages
Book Rating : 4.7/5 (854 download)

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Book Synopsis Advances in Domain Adaptation Theory by : Ievgen Redko

Download or read book Advances in Domain Adaptation Theory written by Ievgen Redko and published by ISTE Press - Elsevier. This book was released on 2019-08-14 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning Algorithms and Techniques

Author : Krishna Bonagiri
Publisher : RK Publication
ISBN 13 : 8197469725
Total Pages : 320 pages
Book Rating : 4.1/5 (974 download)

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Book Synopsis Machine Learning Algorithms and Techniques by : Krishna Bonagiri

Download or read book Machine Learning Algorithms and Techniques written by Krishna Bonagiri and published by RK Publication. This book was released on 2024-06-21 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Algorithms and Techniques the concepts, popular algorithms, and essential techniques of machine learning. A comprehensive covering supervised, unsupervised, and reinforcement learning methods while exploring key algorithms like decision trees, neural networks, clustering, and more. Practical applications and examples bring each algorithm to life, helping readers understand how these models are used to solve real-world problems. Designed for both beginners and experienced practitioners, this book is an ideal guide for mastering the fundamentals and applications of machine learning.

Metric Learning

Author : Aurélien Muise
Publisher : Springer Nature
ISBN 13 : 303101572X
Total Pages : 139 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Metric Learning by : Aurélien Muise

Download or read book Metric Learning written by Aurélien Muise and published by Springer Nature. This book was released on 2022-05-31 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies

Intelligent Systems and Applications

Author : Kohei Arai
Publisher : Springer Nature
ISBN 13 : 303116072X
Total Pages : 832 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Intelligent Systems and Applications by : Kohei Arai

Download or read book Intelligent Systems and Applications written by Kohei Arai and published by Springer Nature. This book was released on 2022-08-30 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a remarkable collection of chapters covering a wide domain of topics related to artificial intelligence and its applications to the real world. The conference attracted a total of 494 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-reviewed process. Of the total submissions, 176 submissions have been selected to be included in these proceedings. It is difficult to imagine how artificial intelligence has become an inseparable part of our life. From mobile phones, smart watches, washing machines to smart homes, smart cars, and smart industries, artificial intelligence has helped to revolutionize the whole globe. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. Distinguished researchers have made valuable studies to understand the various bottlenecks existing in different arenas and how they can be overcome with the use of intelligent systems. This book also provides new directions and dimensions of future research work. We hope that readers find the volume interesting and valuable.

Domain Adaptation in Computer Vision with Deep Learning

Author : Hemanth Venkateswara
Publisher : Springer Nature
ISBN 13 : 3030455297
Total Pages : 256 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Domain Adaptation in Computer Vision with Deep Learning by : Hemanth Venkateswara

Download or read book Domain Adaptation in Computer Vision with Deep Learning written by Hemanth Venkateswara and published by Springer Nature. This book was released on 2020-08-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computer Vision – ECCV 2018

Author : Vittorio Ferrari
Publisher : Springer
ISBN 13 : 9783030012632
Total Pages : 853 pages
Book Rating : 4.0/5 (126 download)

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Book Synopsis Computer Vision – ECCV 2018 by : Vittorio Ferrari

Download or read book Computer Vision – ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-09 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Author : Irena Koprinska
Publisher : Springer Nature
ISBN 13 : 3031236335
Total Pages : 499 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Machine Learning and Principles and Practice of Knowledge Discovery in Databases by : Irena Koprinska

Download or read book Machine Learning and Principles and Practice of Knowledge Discovery in Databases written by Irena Koprinska and published by Springer Nature. This book was released on 2023-01-30 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following five workshops: Workshop on Data Science for Social Good (SoGood 2022) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022) Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022) Workshop on Uplift Modeling (UMOD 2022) Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022) Workshop on Mining Data for Financial Application (MIDAS 2022) Workshop on Machine Learning for Cybersecurity (MLCS 2022) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) Workshop on Data Analysis in Life Science (DALS 2022) Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022)

Toward Artificial General Intelligence

Author : Victor Hugo C. de Albuquerque
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3111323749
Total Pages : 424 pages
Book Rating : 4.1/5 (113 download)

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Book Synopsis Toward Artificial General Intelligence by : Victor Hugo C. de Albuquerque

Download or read book Toward Artificial General Intelligence written by Victor Hugo C. de Albuquerque and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-11-06 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning in Non-Stationary Environments

Author : Masashi Sugiyama
Publisher : MIT Press
ISBN 13 : 0262300435
Total Pages : 279 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Machine Learning in Non-Stationary Environments by : Masashi Sugiyama

Download or read book Machine Learning in Non-Stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012-03-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

How Learning Works

Author : Susan A. Ambrose
Publisher : John Wiley & Sons
ISBN 13 : 0470617608
Total Pages : 336 pages
Book Rating : 4.4/5 (76 download)

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Book Synopsis How Learning Works by : Susan A. Ambrose

Download or read book How Learning Works written by Susan A. Ambrose and published by John Wiley & Sons. This book was released on 2010-04-16 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for How Learning Works "How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning." —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching "This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching." —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education "Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues." —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching "As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book." —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning

Ant Colony Optimization

Author : Marco Dorigo
Publisher : MIT Press
ISBN 13 : 9780262042192
Total Pages : 324 pages
Book Rating : 4.0/5 (421 download)

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Book Synopsis Ant Colony Optimization by : Marco Dorigo

Download or read book Ant Colony Optimization written by Marco Dorigo and published by MIT Press. This book was released on 2004-06-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

Introduction to Transfer Learning

Author : Jindong Wang
Publisher : Springer Nature
ISBN 13 : 9811975841
Total Pages : 333 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Introduction to Transfer Learning by : Jindong Wang

Download or read book Introduction to Transfer Learning written by Jindong Wang and published by Springer Nature. This book was released on 2023-03-30 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.