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Biblioteca

MARC Lista (tabellare)
Event attendance prediction in social networks / / Xiaomei Zhang, Guohong Cao
Event attendance prediction in social networks / / Xiaomei Zhang, Guohong Cao
Autore Zhang Xiaomei
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (59 pages)
Disciplina 004.65
Collana SpringerBriefs in statistics
Soggetto topico Mineria de dades
Comunitats virtuals
Context-aware computing
Data mining
Soggetto genere / forma Llibres electrònics
ISBN 3-030-89262-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520081903321
Zhang Xiaomei  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fuzzy, rough and intuitionistic fuzzy set approaches for data handling : theory and applications / / edited by Tanmoy Som [and three others]
Fuzzy, rough and intuitionistic fuzzy set approaches for data handling : theory and applications / / edited by Tanmoy Som [and three others]
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer, , [2023]
Descrizione fisica 1 online resource (279 pages)
Disciplina 943.005
Collana Forum for Interdisciplinary Mathematics
Soggetto topico Data mining
Conjunts borrosos
Mineria de dades
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 9789811985669
9789811985652
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Fuzzy Sets and Rough Sets: A Mathematical Narrative -- Enhancing the Prediction of Anti-cancer Peptides by Suitable Feature Extraction and FRFS with ACO Search Followed by Resampling -- New Methods of Vagueness and Uncertainty Quantification in Lattice Boltzmann Method based Solute Transport Model -- Fuzzy Rough Set-based Feature Selection for Text Categorization -- An Extensive Survey on Detection of Malaria Parasites in Patients based on Fuzzy Approaches -- Application of Feature Extraction and Feature Selection followed by SMOTE to Improve the Prediction of DNA Binding Proteins -- Perspectives of Soft Computing in Multiscale Fluid Flow Systems -- Various Generalizations of Fuzzy Sets in the Context of Soft Computing and Decision Making -- A Linear Diophantine Fuzzy Soft Set based Decision Making Approach using Revised Max-Min Average Composition Method -- Recent Developments in Fuzzy Dynamic Data Envelopment Analysis and Its Applications -- Role of Centrality Measures in Link Prediction on Fuzzy Social Networks -- Interval Solutions of Fractional Integro-Differential Equations by using Modifed Adomian Decomposition Method -- Generalized Hukuhara Subdifferentiability for Convex Interval-valued Functions and its Applications in Nonsmooth Interval Optimization -- Rules Based Classifier for Identifying Fake Reviews in E-Commerce: Deep Learning System.
Record Nr. UNINA-9910683348503321
Singapore : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu
Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu
Autore Wu Huijun (Writer on cloud computing)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (211 pages) : illustrations
Disciplina 004.3
Soggetto topico Information retrieval
Data mining
Big data
Information organization
Recuperació de la informació
Mineria de dades
Dades massives
Soggetto genere / forma Llibres electrònics
ISBN 3-030-60094-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Who This Book Is For -- How This Book Is Organized -- What You Need for This Book -- Typographical Conventions -- Acknowledgments -- Contents -- About the Authors -- Part I Heron Fundamentals -- 1 Stream Processing -- 1.1 Big Data Processing -- 1.1.1 Lambda Architecture -- 1.1.1.1 Batch Processing Layer -- 1.1.1.2 Stream Processing Layer -- 1.1.1.3 Serving Layer -- 1.1.2 Kappa Architecture -- 1.2 Big Data Stream Processing -- 1.3 From Apache Storm to Apache Heron (Incubating) -- 1.3.1 Motivation for Heron -- 1.3.2 Heron Design Goals -- 1.3.3 Join the Apache Heron (Incubating) Community -- 1.4 Stream Processing Tools -- 1.5 Summary -- References -- 2 Heron Basics -- 2.1 Topology Data Model -- 2.1.1 Topology -- 2.1.2 Spout -- 2.1.3 Bolt -- 2.1.4 Grouping -- 2.2 Heron Architecture and Components -- 2.2.1 Cluster-Level Components (Six Components) -- 2.2.1.1 Scheduler -- 2.2.1.2 State Manager -- 2.2.1.3 Uploader -- 2.2.1.4 Heron CLI -- 2.2.1.5 Heron Tracker -- 2.2.1.6 Heron UI -- 2.2.2 Topology-Level Components (Four Components) -- 2.2.2.1 Heron Instance -- 2.2.2.2 Stream Manager -- 2.2.2.3 Topology Master -- 2.2.2.4 Metrics Manager -- 2.3 Submission Process and Failure Handling -- 2.4 Submit the First Topology -- 2.4.1 Preparation -- 2.4.2 Install the Heron Client -- 2.4.3 Heron Example Topologies -- 2.4.4 Submit the Topology JAR File -- 2.4.5 Observe the Running Topology -- 2.5 Summary -- References -- 3 Study Heron Code -- 3.1 Code Languages -- 3.2 Requirements for Compiling -- 3.3 Prepare the Compiling Environment -- 3.4 Source Organization -- 3.4.1 Directory Organization -- 3.4.2 Bazel Perspective -- 3.5 Compile Heron -- 3.6 Examine Compiling Results -- 3.6.1 Examine the API -- 3.6.2 Examine Packages -- 3.7 Run Tests -- 3.7.1 Unit Test -- 3.7.2 Integration Test -- 3.8 Summary -- References.
Part II Write Heron Topologies -- 4 Migrate Storm Topology to Heron -- 4.1 Prepare the Storm Topology Code -- 4.1.1 Examine the Storm Topology Code -- 4.1.2 Examine the Storm Flux Code -- 4.2 Migrate the Storm Topology Code to a HeronTopology Project -- 4.2.1 Adjust the Topology Java Code -- 4.2.2 Adjust the Project File pom.xml -- 4.2.2.1 Add Dependency -- 4.2.2.2 Build with Dependencies -- 4.2.3 Compile the Topology JAR File -- 4.3 Migrate Storm Flux to Heron ECO -- 4.4 Summary -- References -- 5 Write Topology Code -- 5.1 Before Writing Code -- 5.1.1 Design Topology -- 5.1.2 Choose a Heron API -- 5.2 Write Topology in Java -- 5.2.1 Code the Topology -- 5.2.1.1 Code Main -- 5.2.1.2 Code Spout -- 5.2.1.3 Code Bolt -- 5.2.2 Understand Tuple Flow -- 5.2.2.1 How Tuple Is Constructed -- 5.2.2.2 How Tuple Is Routed -- 5.3 Write Topology in Python -- 5.3.1 Code Main -- 5.3.2 Code Spout -- 5.3.3 Code Bolt -- 5.3.4 Compile and Run -- 5.4 Summary -- Reference -- 6 Heron Topology Features -- 6.1 Delivery Semantics -- 6.1.1 At-Least-Once -- 6.1.2 Effectively-Once -- 6.1.2.1 Requirements for Effectively-Once -- 6.1.2.2 Exactly-Once Versus Effectively-Once -- 6.1.2.3 Stateful Topologies -- 6.1.2.4 Implement Effectively-Once -- 6.2 Windowing -- 6.2.1 Windowing Concepts -- 6.2.2 Windowing Example -- 6.3 Summary -- Reference -- 7 Heron Streamlet API -- 7.1 Streamlet API Concepts -- 7.1.1 Streamlets -- 7.1.2 Operations -- 7.2 Write a Processing Graph with the Java Streamlet API -- 7.2.1 Sources -- 7.2.2 Sinks -- 7.2.3 Transform: Filter, Map, FlatMap -- 7.2.4 Partitioning -- 7.2.5 Clone and Union -- 7.2.6 Reduce by Key and Window -- 7.2.7 Join -- 7.2.8 Configuration -- 7.3 Write a Processing Graph with the Python Streamlet API -- 7.3.1 Source Generator -- 7.3.2 Processing Graph Construction -- 7.4 Write a Processing Graph with the Scala Streamlet API.
7.4.1 Install sbt -- 7.4.2 Source Directory -- 7.4.3 Compose Processing Graph -- 7.4.4 Examine the JAR File -- 7.5 Summary -- References -- Part III Operate Heron Clusters -- 8 Manage a Topology -- 8.1 Install Heron Client -- 8.1.1 What Is Inside heron-core.tar.gz -- 8.1.2 YAML Configuration -- 8.2 Run Topology -- 8.2.1 Topology Life Cycle -- 8.2.2 Submit Topology -- 8.2.3 Observe the Topology Running Status -- 8.3 Explore the ``heron'' Command -- 8.3.1 Common Arguments and Optional Flags -- 8.3.2 Explore ``heron submit'' Options -- 8.3.3 Kill Topology -- 8.3.4 Activate and Deactivate Topology -- 8.3.5 Restart Topology -- 8.3.6 Update Topology -- 8.4 Summary -- References -- 9 Manage Multiple Topologies -- 9.1 Install Heron Tools -- 9.2 Heron Tracker -- 9.3 Heron UI -- 9.4 Heron Explorer -- 9.5 Summary -- Reference -- Part IV Heron Insights -- 10 Explore Heron -- 10.1 Heron Processes -- 10.1.1 Java Processes -- 10.1.2 C++ Processes -- 10.1.3 Python Processes -- 10.2 State Manager -- 10.3 Heron Scheduler -- 10.3.1 Three Plans: Logical, Packing, and Physical -- 10.3.2 Restart Dead Processes -- 10.4 Data Flow -- 10.4.1 Capture Packets -- 10.4.2 Communication Primitive -- 10.5 Metrics System -- 10.5.1 File Sink -- 10.5.2 MetricsCache Manager Sink and TopologyMaster Sink -- 10.6 Summary -- 11 Extending the Heron Metrics Sink -- 11.1 Time Series -- 11.2 Metrics Category -- 11.3 Customize a Metrics Sink -- 11.3.1 Metrics Sink SPI -- 11.3.2 Metrics Sink Configuration -- 11.4 MySQL Metrics Sink -- 11.4.1 Implement IMetricsSink -- 11.4.2 Configure the Metrics Sink -- 11.4.3 Observe Metrics -- 11.5 Summary -- Reference -- 12 Extending Heron Scheduler -- 12.1 Scheduler SPI -- 12.1.1 ILauncher and IScheduler Work Together -- 12.1.2 ILauncher -- 12.1.3 IScheduler -- 12.2 Timeout Scheduler -- 12.2.1 Timeout Launcher -- 12.2.1.1 Prepare Container.
12.2.1.2 Launch by Service -- 12.2.1.3 Launch by Library -- 12.2.2 Timeout Scheduler -- 12.2.2.1 Abstract Parent Class -- 12.2.2.2 Service Mode -- 12.2.2.3 Library Mode -- 12.2.2.4 Service Mode Versus Library Mode -- 12.3 Summary -- 13 Heron Is Evolving -- 13.1 Dhalion and Health Manager -- 13.1.1 Dhalion -- 13.1.2 Health Manager -- 13.2 Deploy Mode (API Server) -- 13.3 Cloud-Native Heron -- 13.4 Summary -- Reference -- Index.
Record Nr. UNINA-9910485028003321
Wu Huijun (Writer on cloud computing)  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu
Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu
Autore Wu Huijun (Writer on cloud computing)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (211 pages) : illustrations
Disciplina 004.3
Soggetto topico Information retrieval
Data mining
Big data
Information organization
Recuperació de la informació
Mineria de dades
Dades massives
Soggetto genere / forma Llibres electrònics
ISBN 3-030-60094-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Who This Book Is For -- How This Book Is Organized -- What You Need for This Book -- Typographical Conventions -- Acknowledgments -- Contents -- About the Authors -- Part I Heron Fundamentals -- 1 Stream Processing -- 1.1 Big Data Processing -- 1.1.1 Lambda Architecture -- 1.1.1.1 Batch Processing Layer -- 1.1.1.2 Stream Processing Layer -- 1.1.1.3 Serving Layer -- 1.1.2 Kappa Architecture -- 1.2 Big Data Stream Processing -- 1.3 From Apache Storm to Apache Heron (Incubating) -- 1.3.1 Motivation for Heron -- 1.3.2 Heron Design Goals -- 1.3.3 Join the Apache Heron (Incubating) Community -- 1.4 Stream Processing Tools -- 1.5 Summary -- References -- 2 Heron Basics -- 2.1 Topology Data Model -- 2.1.1 Topology -- 2.1.2 Spout -- 2.1.3 Bolt -- 2.1.4 Grouping -- 2.2 Heron Architecture and Components -- 2.2.1 Cluster-Level Components (Six Components) -- 2.2.1.1 Scheduler -- 2.2.1.2 State Manager -- 2.2.1.3 Uploader -- 2.2.1.4 Heron CLI -- 2.2.1.5 Heron Tracker -- 2.2.1.6 Heron UI -- 2.2.2 Topology-Level Components (Four Components) -- 2.2.2.1 Heron Instance -- 2.2.2.2 Stream Manager -- 2.2.2.3 Topology Master -- 2.2.2.4 Metrics Manager -- 2.3 Submission Process and Failure Handling -- 2.4 Submit the First Topology -- 2.4.1 Preparation -- 2.4.2 Install the Heron Client -- 2.4.3 Heron Example Topologies -- 2.4.4 Submit the Topology JAR File -- 2.4.5 Observe the Running Topology -- 2.5 Summary -- References -- 3 Study Heron Code -- 3.1 Code Languages -- 3.2 Requirements for Compiling -- 3.3 Prepare the Compiling Environment -- 3.4 Source Organization -- 3.4.1 Directory Organization -- 3.4.2 Bazel Perspective -- 3.5 Compile Heron -- 3.6 Examine Compiling Results -- 3.6.1 Examine the API -- 3.6.2 Examine Packages -- 3.7 Run Tests -- 3.7.1 Unit Test -- 3.7.2 Integration Test -- 3.8 Summary -- References.
Part II Write Heron Topologies -- 4 Migrate Storm Topology to Heron -- 4.1 Prepare the Storm Topology Code -- 4.1.1 Examine the Storm Topology Code -- 4.1.2 Examine the Storm Flux Code -- 4.2 Migrate the Storm Topology Code to a HeronTopology Project -- 4.2.1 Adjust the Topology Java Code -- 4.2.2 Adjust the Project File pom.xml -- 4.2.2.1 Add Dependency -- 4.2.2.2 Build with Dependencies -- 4.2.3 Compile the Topology JAR File -- 4.3 Migrate Storm Flux to Heron ECO -- 4.4 Summary -- References -- 5 Write Topology Code -- 5.1 Before Writing Code -- 5.1.1 Design Topology -- 5.1.2 Choose a Heron API -- 5.2 Write Topology in Java -- 5.2.1 Code the Topology -- 5.2.1.1 Code Main -- 5.2.1.2 Code Spout -- 5.2.1.3 Code Bolt -- 5.2.2 Understand Tuple Flow -- 5.2.2.1 How Tuple Is Constructed -- 5.2.2.2 How Tuple Is Routed -- 5.3 Write Topology in Python -- 5.3.1 Code Main -- 5.3.2 Code Spout -- 5.3.3 Code Bolt -- 5.3.4 Compile and Run -- 5.4 Summary -- Reference -- 6 Heron Topology Features -- 6.1 Delivery Semantics -- 6.1.1 At-Least-Once -- 6.1.2 Effectively-Once -- 6.1.2.1 Requirements for Effectively-Once -- 6.1.2.2 Exactly-Once Versus Effectively-Once -- 6.1.2.3 Stateful Topologies -- 6.1.2.4 Implement Effectively-Once -- 6.2 Windowing -- 6.2.1 Windowing Concepts -- 6.2.2 Windowing Example -- 6.3 Summary -- Reference -- 7 Heron Streamlet API -- 7.1 Streamlet API Concepts -- 7.1.1 Streamlets -- 7.1.2 Operations -- 7.2 Write a Processing Graph with the Java Streamlet API -- 7.2.1 Sources -- 7.2.2 Sinks -- 7.2.3 Transform: Filter, Map, FlatMap -- 7.2.4 Partitioning -- 7.2.5 Clone and Union -- 7.2.6 Reduce by Key and Window -- 7.2.7 Join -- 7.2.8 Configuration -- 7.3 Write a Processing Graph with the Python Streamlet API -- 7.3.1 Source Generator -- 7.3.2 Processing Graph Construction -- 7.4 Write a Processing Graph with the Scala Streamlet API.
7.4.1 Install sbt -- 7.4.2 Source Directory -- 7.4.3 Compose Processing Graph -- 7.4.4 Examine the JAR File -- 7.5 Summary -- References -- Part III Operate Heron Clusters -- 8 Manage a Topology -- 8.1 Install Heron Client -- 8.1.1 What Is Inside heron-core.tar.gz -- 8.1.2 YAML Configuration -- 8.2 Run Topology -- 8.2.1 Topology Life Cycle -- 8.2.2 Submit Topology -- 8.2.3 Observe the Topology Running Status -- 8.3 Explore the ``heron'' Command -- 8.3.1 Common Arguments and Optional Flags -- 8.3.2 Explore ``heron submit'' Options -- 8.3.3 Kill Topology -- 8.3.4 Activate and Deactivate Topology -- 8.3.5 Restart Topology -- 8.3.6 Update Topology -- 8.4 Summary -- References -- 9 Manage Multiple Topologies -- 9.1 Install Heron Tools -- 9.2 Heron Tracker -- 9.3 Heron UI -- 9.4 Heron Explorer -- 9.5 Summary -- Reference -- Part IV Heron Insights -- 10 Explore Heron -- 10.1 Heron Processes -- 10.1.1 Java Processes -- 10.1.2 C++ Processes -- 10.1.3 Python Processes -- 10.2 State Manager -- 10.3 Heron Scheduler -- 10.3.1 Three Plans: Logical, Packing, and Physical -- 10.3.2 Restart Dead Processes -- 10.4 Data Flow -- 10.4.1 Capture Packets -- 10.4.2 Communication Primitive -- 10.5 Metrics System -- 10.5.1 File Sink -- 10.5.2 MetricsCache Manager Sink and TopologyMaster Sink -- 10.6 Summary -- 11 Extending the Heron Metrics Sink -- 11.1 Time Series -- 11.2 Metrics Category -- 11.3 Customize a Metrics Sink -- 11.3.1 Metrics Sink SPI -- 11.3.2 Metrics Sink Configuration -- 11.4 MySQL Metrics Sink -- 11.4.1 Implement IMetricsSink -- 11.4.2 Configure the Metrics Sink -- 11.4.3 Observe Metrics -- 11.5 Summary -- Reference -- 12 Extending Heron Scheduler -- 12.1 Scheduler SPI -- 12.1.1 ILauncher and IScheduler Work Together -- 12.1.2 ILauncher -- 12.1.3 IScheduler -- 12.2 Timeout Scheduler -- 12.2.1 Timeout Launcher -- 12.2.1.1 Prepare Container.
12.2.1.2 Launch by Service -- 12.2.1.3 Launch by Library -- 12.2.2 Timeout Scheduler -- 12.2.2.1 Abstract Parent Class -- 12.2.2.2 Service Mode -- 12.2.2.3 Library Mode -- 12.2.2.4 Service Mode Versus Library Mode -- 12.3 Summary -- 13 Heron Is Evolving -- 13.1 Dhalion and Health Manager -- 13.1.1 Dhalion -- 13.1.2 Health Manager -- 13.2 Deploy Mode (API Server) -- 13.3 Cloud-Native Heron -- 13.4 Summary -- Reference -- Index.
Record Nr. UNISA-996466544503316
Wu Huijun (Writer on cloud computing)  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Knowledge Management in Digital Change [[electronic resource] ] : New Findings and Practical Cases / / edited by Klaus North, Ronald Maier, Oliver Haas
Knowledge Management in Digital Change [[electronic resource] ] : New Findings and Practical Cases / / edited by Klaus North, Ronald Maier, Oliver Haas
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (393 pages) : illustrations
Disciplina 650
Collana Progress in IS
Soggetto topico Knowledge management
Data mining
Business information services
User interfaces (Computer systems)
Human-computer interaction
Electronic data processing - Management
Industrial organization
Knowledge Management
Data Mining and Knowledge Discovery
Business Information Systems
User Interfaces and Human Computer Interaction
IT Operations
Organization
Gestió del coneixement
Gestió de la informació
Mineria de dades
Informàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-319-73546-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction: The Knowledge Ladder 4.0 - Value Creation in the Digitally Enabled Economy -- Part I: Digitally Enabled Enrichment of Resources to Leverage Human Performance -- Part II: Collaboration and Networking -- Part III: Leading and Learning 4.0 -- Part IV: New Forms of Knowledge-intensive Digitally Enabled Value Creation. .
Record Nr. UNINA-9910298207903321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Mathematical foundations for data analysis / / Jeff M. Phillips
Mathematical foundations for data analysis / / Jeff M. Phillips
Autore Phillips Jeff M.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (299 pages)
Disciplina 006.312
Collana Springer Series in the Data Sciences
Soggetto topico Data mining - Mathematics
Machine learning - Mathematics
Mineria de dades
Aprenentatge automàtic
Matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-62341-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgements -- Contents -- 1 Probability Review -- 1.1 Sample Spaces -- 1.2 Conditional Probability and Independence -- 1.3 Density Functions -- 1.4 Expected Value -- 1.5 Variance -- 1.6 Joint, Marginal, and Conditional Distributions -- 1.7 Bayes' Rule -- 1.7.1 Model Given Data -- 1.8 Bayesian Inference -- Exercises -- 2 Convergence and Sampling -- 2.1 Sampling and Estimation -- 2.2 Probably Approximately Correct (PAC) -- 2.3 Concentration of Measure -- 2.3.1 Markov Inequality -- 2.3.2 Chebyshev Inequality -- 2.3.3 Chernoff-Hoeffding Inequality -- 2.3.4 Union Bound and Examples -- 2.4 Importance Sampling -- 2.4.1 Sampling Without Replacement with Priority Sampling -- Exercises -- 3 Linear Algebra Review -- 3.1 Vectors and Matrices -- 3.2 Addition and Multiplication -- 3.3 Norms -- 3.4 Linear Independence -- 3.5 Rank -- 3.6 Square Matrices and Properties -- 3.7 Orthogonality -- Exercises -- 4 Distances and Nearest Neighbors -- 4.1 Metrics -- 4.2 Lp Distances and their Relatives -- 4.2.1 Lp Distances -- 4.2.2 Mahalanobis Distance -- 4.2.3 Cosine and Angular Distance -- 4.2.4 KL Divergence -- 4.3 Distances for Sets and Strings -- 4.3.1 Jaccard Distance -- 4.3.2 Edit Distance -- 4.4 Modeling Text with Distances -- 4.4.1 Bag-of-Words Vectors -- 4.4.2 k-Grams -- 4.5 Similarities -- 4.5.1 Set Similarities -- 4.5.2 Normed Similarities -- 4.5.3 Normed Similarities between Sets -- 4.6 Locality Sensitive Hashing -- 4.6.1 Properties of Locality Sensitive Hashing -- 4.6.2 Prototypical Tasks for LSH -- 4.6.3 Banding to Amplify LSH -- 4.6.4 LSH for Angular Distance -- 4.6.5 LSH for Euclidean Distance -- 4.6.6 Min Hashing as LSH for Jaccard Distance -- Exercises -- 5 Linear Regression -- 5.1 Simple Linear Regression -- 5.2 Linear Regression with Multiple Explanatory Variables -- 5.3 Polynomial Regression -- 5.4 Cross-Validation.
5.4.1 Other ways to Evaluate Linear Regression Models -- 5.5 Regularized Regression -- 5.5.1 Tikhonov Regularization for Ridge Regression -- 5.5.2 Lasso -- 5.5.3 Dual Constrained Formulation -- 5.5.4 Matching Pursuit -- Exercises -- 6 Gradient Descent -- 6.1 Functions -- 6.2 Gradients -- 6.3 Gradient Descent -- 6.3.1 Learning Rate -- 6.4 Fitting a Model to Data -- 6.4.1 Least Mean Squares Updates for Regression -- 6.4.2 Decomposable Functions -- Exercises -- 7 Dimensionality Reduction -- 7.1 Data Matrices -- 7.1.1 Projections -- 7.1.2 Sum of Squared Errors Goal -- 7.2 Singular Value Decomposition -- 7.2.1 Best Rank-k Approximation of a Matrix -- 7.3 Eigenvalues and Eigenvectors -- 7.4 The Power Method -- 7.5 Principal Component Analysis -- 7.6 Multidimensional Scaling -- 7.6.1 Why does Classical MDS work? -- 7.7 Linear Discriminant Analysis -- 7.8 Distance Metric Learning -- 7.9 Matrix Completion -- 7.10 Random Projections -- Exercises -- 8 Clustering -- 8.1 Voronoi Diagrams -- 8.1.1 Delaunay Triangulation -- 8.1.2 Connection to Assignment-Based Clustering -- 8.2 Gonzalez's Algorithm for k-Center Clustering -- 8.3 Lloyd's Algorithm for k-Means Clustering -- 8.3.1 Lloyd's Algorithm -- 8.3.2 k-Means++ -- 8.3.3 k-Mediod Clustering -- 8.3.4 Soft Clustering -- 8.4 Mixture of Gaussians -- 8.4.1 Expectation-Maximization -- 8.5 Hierarchical Clustering -- 8.6 Density-Based Clustering and Outliers -- 8.6.1 Outliers -- 8.7 Mean Shift Clustering -- Exercises -- 9 Classification -- 9.1 Linear Classifiers -- 9.1.1 Loss Functions -- 9.1.2 Cross-Validation and Regularization -- 9.2 Perceptron Algorithm -- 9.3 Support Vector Machines and Kernels -- 9.3.1 The Dual: Mistake Counter -- 9.3.2 Feature Expansion -- 9.3.3 Support Vector Machines -- 9.4 Learnability and VC dimension -- 9.5 kNN Classifiers -- 9.6 Decision Trees -- 9.7 Neural Networks.
9.7.1 Training with Back-propagation -- 10 Graph Structured Data -- 10.1 Markov Chains -- 10.1.1 Ergodic Markov Chains -- 10.1.2 Metropolis Algorithm -- 10.2 PageRank -- 10.3 Spectral Clustering on Graphs -- 10.3.1 Laplacians and their EigenStructures -- 10.4 Communities in Graphs -- 10.4.1 Preferential Attachment -- 10.4.2 Betweenness -- 10.4.3 Modularity -- Exercises -- 11 Big Data and Sketching -- 11.1 The Streaming Model -- 11.1.1 Mean and Variance -- 11.1.2 Reservoir Sampling -- 11.2 Frequent Items -- 11.2.1 Warm-Up: Majority -- 11.2.2 Misra-Gries Algorithm -- 11.2.3 Count-Min Sketch -- 11.2.4 Count Sketch -- 11.3 Matrix Sketching -- 11.3.1 Covariance Matrix Summation -- 11.3.2 Frequent Directions -- 11.3.3 Row Sampling -- 11.3.4 Random Projections and Count Sketch Hashing -- Exercises -- Index.
Record Nr. UNINA-9910483358803321
Phillips Jeff M.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Mathematical foundations for data analysis / / Jeff M. Phillips
Mathematical foundations for data analysis / / Jeff M. Phillips
Autore Phillips Jeff M.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (299 pages)
Disciplina 006.312
Collana Springer Series in the Data Sciences
Soggetto topico Data mining - Mathematics
Machine learning - Mathematics
Mineria de dades
Aprenentatge automàtic
Matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-62341-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgements -- Contents -- 1 Probability Review -- 1.1 Sample Spaces -- 1.2 Conditional Probability and Independence -- 1.3 Density Functions -- 1.4 Expected Value -- 1.5 Variance -- 1.6 Joint, Marginal, and Conditional Distributions -- 1.7 Bayes' Rule -- 1.7.1 Model Given Data -- 1.8 Bayesian Inference -- Exercises -- 2 Convergence and Sampling -- 2.1 Sampling and Estimation -- 2.2 Probably Approximately Correct (PAC) -- 2.3 Concentration of Measure -- 2.3.1 Markov Inequality -- 2.3.2 Chebyshev Inequality -- 2.3.3 Chernoff-Hoeffding Inequality -- 2.3.4 Union Bound and Examples -- 2.4 Importance Sampling -- 2.4.1 Sampling Without Replacement with Priority Sampling -- Exercises -- 3 Linear Algebra Review -- 3.1 Vectors and Matrices -- 3.2 Addition and Multiplication -- 3.3 Norms -- 3.4 Linear Independence -- 3.5 Rank -- 3.6 Square Matrices and Properties -- 3.7 Orthogonality -- Exercises -- 4 Distances and Nearest Neighbors -- 4.1 Metrics -- 4.2 Lp Distances and their Relatives -- 4.2.1 Lp Distances -- 4.2.2 Mahalanobis Distance -- 4.2.3 Cosine and Angular Distance -- 4.2.4 KL Divergence -- 4.3 Distances for Sets and Strings -- 4.3.1 Jaccard Distance -- 4.3.2 Edit Distance -- 4.4 Modeling Text with Distances -- 4.4.1 Bag-of-Words Vectors -- 4.4.2 k-Grams -- 4.5 Similarities -- 4.5.1 Set Similarities -- 4.5.2 Normed Similarities -- 4.5.3 Normed Similarities between Sets -- 4.6 Locality Sensitive Hashing -- 4.6.1 Properties of Locality Sensitive Hashing -- 4.6.2 Prototypical Tasks for LSH -- 4.6.3 Banding to Amplify LSH -- 4.6.4 LSH for Angular Distance -- 4.6.5 LSH for Euclidean Distance -- 4.6.6 Min Hashing as LSH for Jaccard Distance -- Exercises -- 5 Linear Regression -- 5.1 Simple Linear Regression -- 5.2 Linear Regression with Multiple Explanatory Variables -- 5.3 Polynomial Regression -- 5.4 Cross-Validation.
5.4.1 Other ways to Evaluate Linear Regression Models -- 5.5 Regularized Regression -- 5.5.1 Tikhonov Regularization for Ridge Regression -- 5.5.2 Lasso -- 5.5.3 Dual Constrained Formulation -- 5.5.4 Matching Pursuit -- Exercises -- 6 Gradient Descent -- 6.1 Functions -- 6.2 Gradients -- 6.3 Gradient Descent -- 6.3.1 Learning Rate -- 6.4 Fitting a Model to Data -- 6.4.1 Least Mean Squares Updates for Regression -- 6.4.2 Decomposable Functions -- Exercises -- 7 Dimensionality Reduction -- 7.1 Data Matrices -- 7.1.1 Projections -- 7.1.2 Sum of Squared Errors Goal -- 7.2 Singular Value Decomposition -- 7.2.1 Best Rank-k Approximation of a Matrix -- 7.3 Eigenvalues and Eigenvectors -- 7.4 The Power Method -- 7.5 Principal Component Analysis -- 7.6 Multidimensional Scaling -- 7.6.1 Why does Classical MDS work? -- 7.7 Linear Discriminant Analysis -- 7.8 Distance Metric Learning -- 7.9 Matrix Completion -- 7.10 Random Projections -- Exercises -- 8 Clustering -- 8.1 Voronoi Diagrams -- 8.1.1 Delaunay Triangulation -- 8.1.2 Connection to Assignment-Based Clustering -- 8.2 Gonzalez's Algorithm for k-Center Clustering -- 8.3 Lloyd's Algorithm for k-Means Clustering -- 8.3.1 Lloyd's Algorithm -- 8.3.2 k-Means++ -- 8.3.3 k-Mediod Clustering -- 8.3.4 Soft Clustering -- 8.4 Mixture of Gaussians -- 8.4.1 Expectation-Maximization -- 8.5 Hierarchical Clustering -- 8.6 Density-Based Clustering and Outliers -- 8.6.1 Outliers -- 8.7 Mean Shift Clustering -- Exercises -- 9 Classification -- 9.1 Linear Classifiers -- 9.1.1 Loss Functions -- 9.1.2 Cross-Validation and Regularization -- 9.2 Perceptron Algorithm -- 9.3 Support Vector Machines and Kernels -- 9.3.1 The Dual: Mistake Counter -- 9.3.2 Feature Expansion -- 9.3.3 Support Vector Machines -- 9.4 Learnability and VC dimension -- 9.5 kNN Classifiers -- 9.6 Decision Trees -- 9.7 Neural Networks.
9.7.1 Training with Back-propagation -- 10 Graph Structured Data -- 10.1 Markov Chains -- 10.1.1 Ergodic Markov Chains -- 10.1.2 Metropolis Algorithm -- 10.2 PageRank -- 10.3 Spectral Clustering on Graphs -- 10.3.1 Laplacians and their EigenStructures -- 10.4 Communities in Graphs -- 10.4.1 Preferential Attachment -- 10.4.2 Betweenness -- 10.4.3 Modularity -- Exercises -- 11 Big Data and Sketching -- 11.1 The Streaming Model -- 11.1.1 Mean and Variance -- 11.1.2 Reservoir Sampling -- 11.2 Frequent Items -- 11.2.1 Warm-Up: Majority -- 11.2.2 Misra-Gries Algorithm -- 11.2.3 Count-Min Sketch -- 11.2.4 Count Sketch -- 11.3 Matrix Sketching -- 11.3.1 Covariance Matrix Summation -- 11.3.2 Frequent Directions -- 11.3.3 Row Sampling -- 11.3.4 Random Projections and Count Sketch Hashing -- Exercises -- Index.
Record Nr. UNISA-996466554403316
Phillips Jeff M.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Recommender systems in fashion and retail / / Editors, Nima Dokoohak [and three others]
Recommender systems in fashion and retail / / Editors, Nima Dokoohak [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (160 pages) : illustrations
Disciplina 006.31
Collana Lecture Notes in Electrical Engineering
Soggetto topico Computational intelligence
Artificial intelligence
Data mining
Intel·ligència computacional
Intel·ligència artificial
Mineria de dades
Soggetto genere / forma Llibres electrònics
ISBN 3-030-66103-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466548803316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Recommender systems in fashion and retail / / Editors, Nima Dokoohak [and three others]
Recommender systems in fashion and retail / / Editors, Nima Dokoohak [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (160 pages) : illustrations
Disciplina 006.31
Collana Lecture Notes in Electrical Engineering
Soggetto topico Computational intelligence
Artificial intelligence
Data mining
Intel·ligència computacional
Intel·ligència artificial
Mineria de dades
Soggetto genere / forma Llibres electrònics
ISBN 3-030-66103-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910483286703321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Security and Intelligent Information Systems [[electronic resource] ] : International Joint Confererence, SIIS 2011, Warsaw, Poland, June 13-14, 2011, Revised Selected Papers / / edited by Pascal Bouvry, Mieczyslaw A. Klopotek, Franck Leprevost, Malgorzata Marciniak, Agnieszka Mykowiecka, Henryk Rybinski
Security and Intelligent Information Systems [[electronic resource] ] : International Joint Confererence, SIIS 2011, Warsaw, Poland, June 13-14, 2011, Revised Selected Papers / / edited by Pascal Bouvry, Mieczyslaw A. Klopotek, Franck Leprevost, Malgorzata Marciniak, Agnieszka Mykowiecka, Henryk Rybinski
Edizione [1st ed. 2012.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012
Descrizione fisica 1 online resource (XVII, 401 p.)
Disciplina 004.6
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Sistemes de seguretat
Mineria de dades
Tractament del llenguatge natural (Informàtica)
Computer organization
Computers
Computer security
Artificial intelligence
Data encryption (Computer science)
Application software
Computer Systems Organization and Communication Networks
Information Systems and Communication Service
Systems and Data Security
Artificial Intelligence
Cryptology
Information Systems Applications (incl. Internet)
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-642-25261-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996465927203316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui