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Cohesive Subgraph Computation over Large Sparse Graphs / Lijun Chang / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2018)
Titre : Cohesive Subgraph Computation over Large Sparse Graphs : Algorithms, Data Structures, and Programming Techniques Type de document : document électronique Auteurs : Lijun Chang, ; Lu Qin, ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2018 Collection : Springer Series in the Data Sciences, ISSN 2365-5674 Importance : XII, 107 p. 21 illus., 1 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-03599-0 Langues : Anglais (eng) Tags : Algorithms Data structures Computer science Data Structures Résumé : This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book. This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation Note de contenu : Introduction -- Linear Heap Data Structures -- Minimum Degree-based Core Decomposition -- Average Degree-based Densest Subgraph Computation -- Higher-order Structure-based Graph Decomposition -- Edge Connectivity-based Graph Decomposition Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=143514 Cohesive Subgraph Computation over Large Sparse Graphs : Algorithms, Data Structures, and Programming Techniques [document électronique] / Lijun Chang, ; Lu Qin, ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2018 . - XII, 107 p. 21 illus., 1 illus. in color : online resource. - (Springer Series in the Data Sciences, ISSN 2365-5674) .
ISBN : 978-3-030-03599-0
Langues : Anglais (eng)
Tags : Algorithms Data structures Computer science Data Structures Résumé : This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book. This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation Note de contenu : Introduction -- Linear Heap Data Structures -- Minimum Degree-based Core Decomposition -- Average Degree-based Densest Subgraph Computation -- Higher-order Structure-based Graph Decomposition -- Edge Connectivity-based Graph Decomposition Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=143514 Data Science for Public Policy / Jeffrey C. Chen / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2021)
Titre : Data Science for Public Policy Type de document : document électronique Auteurs : Jeffrey C. Chen , ; Edward A. Rubin, ; SpringerLink (Online service) ; Gary J. Cornwall, Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2021 Collection : Springer Series in the Data Sciences, ISSN 2365-5674 Importance : XIV, 363 p. 123 illus., 111 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-71352-2 Langues : Anglais (eng) Tags : Computer mathematics Statistics Computational Mathematics and Numerical Analysis Statistics and Computing/Statistics Programs Résumé : This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data Note de contenu : An Introduction -- The Case for Programming -- Elements of Programming -- Transforming Data -- Record Linkage -- Exploratory Data Analysis -- Regression Analysis -- Framing Classification -- Three Quantitative Perspectives -- Prediction -- Cluster Analysis -- Spatial Data -- Natural Language -- The Ethics of Data Science -- Developing Data Products -- Building Data Teams -- Appendix A: Planning a Data Product -- Appendix B: Interview Questions Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=166029 Data Science for Public Policy [document électronique] / Jeffrey C. Chen , ; Edward A. Rubin, ; SpringerLink (Online service) ; Gary J. Cornwall, . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2021 . - XIV, 363 p. 123 illus., 111 illus. in color : online resource. - (Springer Series in the Data Sciences, ISSN 2365-5674) .
ISBN : 978-3-030-71352-2
Langues : Anglais (eng)
Tags : Computer mathematics Statistics Computational Mathematics and Numerical Analysis Statistics and Computing/Statistics Programs Résumé : This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data Note de contenu : An Introduction -- The Case for Programming -- Elements of Programming -- Transforming Data -- Record Linkage -- Exploratory Data Analysis -- Regression Analysis -- Framing Classification -- Three Quantitative Perspectives -- Prediction -- Cluster Analysis -- Spatial Data -- Natural Language -- The Ethics of Data Science -- Developing Data Products -- Building Data Teams -- Appendix A: Planning a Data Product -- Appendix B: Interview Questions Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=166029 Deep Learning Architectures / Ovidiu Calin / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2020)
Titre : Deep Learning Architectures : A Mathematical Approach Type de document : document électronique Auteurs : Ovidiu Calin, ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2020 Collection : Springer Series in the Data Sciences, ISSN 2365-5674 Importance : XXX, 760 p. 213 illus., 35 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-36721-3 Langues : Anglais (eng) Tags : Computer science-Mathematics Computer mathematics Machine learning Mathematical Applications in Computer Science Machine Learning Résumé : This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. Note de contenu : Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions. Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=149872 Deep Learning Architectures : A Mathematical Approach [document électronique] / Ovidiu Calin, ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2020 . - XXX, 760 p. 213 illus., 35 illus. in color : online resource. - (Springer Series in the Data Sciences, ISSN 2365-5674) .
ISBN : 978-3-030-36721-3
Langues : Anglais (eng)
Tags : Computer science-Mathematics Computer mathematics Machine learning Mathematical Applications in Computer Science Machine Learning Résumé : This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. Note de contenu : Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions. Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=149872 First-order and Stochastic Optimization Methods for Machine Learning / Guanghui Lan / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2020)
Titre : First-order and Stochastic Optimization Methods for Machine Learning Type de document : document électronique Auteurs : Guanghui Lan, ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2020 Collection : Springer Series in the Data Sciences, ISSN 2365-5674 Importance : XIII, 582 p. 18 illus., 16 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-39568-1 Langues : Anglais (eng) Tags : Mathematical optimization Machine learning Optimization Machine Learning Résumé : This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning Note de contenu : Machine Learning Models -- Convex Optimization Theory -- Deterministic Convex Optimization -- Stochastic Convex Optimization -- Convex Finite-sum and Distributed Optimization -- Nonconvex Optimization -- Projection-free Methods -- Operator Sliding and Decentralized Optimization Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=155213 First-order and Stochastic Optimization Methods for Machine Learning [document électronique] / Guanghui Lan, ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2020 . - XIII, 582 p. 18 illus., 16 illus. in color : online resource. - (Springer Series in the Data Sciences, ISSN 2365-5674) .
ISBN : 978-3-030-39568-1
Langues : Anglais (eng)
Tags : Mathematical optimization Machine learning Optimization Machine Learning Résumé : This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning Note de contenu : Machine Learning Models -- Convex Optimization Theory -- Deterministic Convex Optimization -- Stochastic Convex Optimization -- Convex Finite-sum and Distributed Optimization -- Nonconvex Optimization -- Projection-free Methods -- Operator Sliding and Decentralized Optimization Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=155213 Lectures on the Nearest Neighbor Method / Gérard Biau / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2015)
Titre : Lectures on the Nearest Neighbor Method Type de document : document électronique Auteurs : Gérard Biau ; SpringerLink (Online service) ; Luc Devroye Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2015 Collection : Springer Series in the Data Sciences, ISSN 2365-5674 Importance : IX, 290 p. 4 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-319-25388-6 Langues : Anglais (eng) Tags : Mathematics Pattern recognition Probabilities Statistics Probability Theory and Stochastic Processes Pattern Recognition Statistics and Computing Statistics Programs Résumé : This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal). Note de contenu : Part I: Density Estimation -- Order Statistics and Nearest Neighbors -- The Expected Nearest Neighbor Distance -- The k-nearest Neighbor Density Estimate -- Uniform Consistency -- Weighted k-nearest neighbor density estimates.- Local Behavior -- Entropy Estimation -- Part II: Regression Estimation -- The Nearest Neighbor Regression Function Estimate -- The 1-nearest Neighbor Regression Function Estimate -- LP-consistency and Stone's Theorem -- Pointwise Consistency -- Uniform Consistency -- Advanced Properties of Uniform Order Statistics -- Rates of Convergence -- Regression: The Noisless Case -- The Choice of a Nearest Neighbor Estimate -- Part III: Supervised Classification -- Basics of Classification -- The 1-nearest Neighbor Classification Rule -- The Nearest Neighbor Classification Rule. Appendix -- Index. Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=118279 Lectures on the Nearest Neighbor Method [document électronique] / Gérard Biau ; SpringerLink (Online service) ; Luc Devroye . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2015 . - IX, 290 p. 4 illus. in color : online resource. - (Springer Series in the Data Sciences, ISSN 2365-5674) .
ISBN : 978-3-319-25388-6
Langues : Anglais (eng)
Tags : Mathematics Pattern recognition Probabilities Statistics Probability Theory and Stochastic Processes Pattern Recognition Statistics and Computing Statistics Programs Résumé : This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal). Note de contenu : Part I: Density Estimation -- Order Statistics and Nearest Neighbors -- The Expected Nearest Neighbor Distance -- The k-nearest Neighbor Density Estimate -- Uniform Consistency -- Weighted k-nearest neighbor density estimates.- Local Behavior -- Entropy Estimation -- Part II: Regression Estimation -- The Nearest Neighbor Regression Function Estimate -- The 1-nearest Neighbor Regression Function Estimate -- LP-consistency and Stone's Theorem -- Pointwise Consistency -- Uniform Consistency -- Advanced Properties of Uniform Order Statistics -- Rates of Convergence -- Regression: The Noisless Case -- The Choice of a Nearest Neighbor Estimate -- Part III: Supervised Classification -- Basics of Classification -- The 1-nearest Neighbor Classification Rule -- The Nearest Neighbor Classification Rule. Appendix -- Index. Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=118279 Lectures on the nearest neighbor method / Gérard Biau / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2015)
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