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Biblioteca

MARC Lista (tabellare)
Geometry of deep learning : a signal processing perspective / / Jong Chul Ye
Geometry of deep learning : a signal processing perspective / / Jong Chul Ye
Autore Ye Jong Chul
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (338 pages)
Disciplina 006.31
Collana Mathematics in industry
Soggetto topico Deep learning (Machine learning)
Geometry
Neural networks (Computer science)
Aprenentatge automàtic
Xarxes neuronals (Informàtica)
Geometria
Soggetto genere / forma Llibres electrònics
ISBN 981-16-6045-X
981-16-6046-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996549372503316
Ye Jong Chul  
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Harmonic and applied analysis : from radon transforms to machine learning / / Filippo De Mari and Ernesto De Vito, editors
Harmonic and applied analysis : from radon transforms to machine learning / / Filippo De Mari and Ernesto De Vito, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (316 pages)
Disciplina 006.31
Collana Applied and Numerical Harmonic Analysis
Soggetto topico Machine learning
Anàlisi harmònica
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 3-030-86664-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466566803316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Harmonic and applied analysis : from radon transforms to machine learning / / Filippo De Mari and Ernesto De Vito, editors
Harmonic and applied analysis : from radon transforms to machine learning / / Filippo De Mari and Ernesto De Vito, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (316 pages)
Disciplina 006.31
Collana Applied and Numerical Harmonic Analysis
Soggetto topico Machine learning
Anàlisi harmònica
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 3-030-86664-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910513600403321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (130 pages) : illustrations
Disciplina 658.4038028563
Soggetto topico Industry 4.0
Machine learning - Industrial applications
Industry 4.0 - Statistical methods
Aprenentatge automàtic
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-031-12402-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs -- 1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0 -- 2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees -- 3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis.
3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests -- 3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References.
Record Nr. UNINA-9910619274503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
Interpretability for Industry 4.0 : statistical and machine learning approaches / / Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (130 pages) : illustrations
Disciplina 658.4038028563
Soggetto topico Industry 4.0
Machine learning - Industrial applications
Industry 4.0 - Statistical methods
Aprenentatge automàtic
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-031-12402-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs -- 1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0 -- 2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees -- 3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis.
3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests -- 3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References.
Record Nr. UNISA-996495169103316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Introducing HR analytics with machine learning : empowering practitioners, psychologists, and organizations / / Christopher M. Rosett, Austin Hagerty
Introducing HR analytics with machine learning : empowering practitioners, psychologists, and organizations / / Christopher M. Rosett, Austin Hagerty
Autore Rosett Christopher M.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (266 pages)
Disciplina 658.300285
Soggetto topico Personnel management - Data processing
Machine learning
Gestió de personal
Processament de dades
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 3-030-67626-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910485595603321
Rosett Christopher M.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An Introduction to Artificial Psychology [[electronic resource] ] : Application Fuzzy Set Theory and Deep Machine Learning in Psychological Research using R / / by Hojjatollah Farahani, Marija Blagojević, Parviz Azadfallah, Peter Watson, Forough Esrafilian, Sara Saljoughi
An Introduction to Artificial Psychology [[electronic resource] ] : Application Fuzzy Set Theory and Deep Machine Learning in Psychological Research using R / / by Hojjatollah Farahani, Marija Blagojević, Parviz Azadfallah, Peter Watson, Forough Esrafilian, Sara Saljoughi
Autore Farahani Hojjatollah
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (262 pages)
Disciplina 150.72
Altri autori (Persone) BlagojevićMarija
AzadfallahParviz
WatsonPeter
EsrafilianForough
SaljoughiSara
Soggetto topico Psychology
Cognitive psychology
Cognitive science
Machine learning
Artificial intelligence
Behavioral Sciences and Psychology
Cognitive Psychology
Cognitive Science
Machine Learning
Artificial Intelligence
Intel·ligència artificial
Psicologia cognitiva
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Psychology
ISBN 9783031311727
9783031311710
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction Chapter 1: After Method -- Chapter 2: Overview on Mathematical Basis of Fuzzy Set Theory. - Chapter 3: Fuzzy Inference Systems (FIS) -- Chapter 4: Fuzzy Cognitive Maps(FCM) -- Chapter 5: Network analysis -- Chapter 6: Association Rules Mining and Associative Classification -- Chapter 7: Artificial Neural Network -- Chapter 8: Feature Selection -- Chapter 9: Cluster analysis.
Record Nr. UNINA-9910726276903321
Farahani Hojjatollah  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to pattern recognition and machine learning / / Paul Fieguth
An introduction to pattern recognition and machine learning / / Paul Fieguth
Autore Fieguth Paul
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (481 pages)
Disciplina 006.31
Soggetto topico Machine learning
Pattern perception
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 9783030959951
9783030959937
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Table of Contents -- List of Examples -- List of Algorithms -- Notation -- 1 Overview -- 2 Introduction to Pattern Recognition -- 2.1 What Is Pattern Recognition? -- 2.2 Measured Patterns -- 2.3 Classes -- 2.4 Classification -- 2.5 Types of Classification Problems -- Case Study 2: Biometrics -- Numerical Lab 2: The Iris Dataset -- Further Reading -- Sample Problems -- References -- 3 Learning -- Case Study 3: The Netflix Prize -- Numerical Lab 3: Overfitting and Underfitting -- Summary -- Further Reading -- Sample Problems -- References -- 4 Representing Patterns -- 4.1 Similarity -- 4.2 Class Shape -- 4.3 Cluster Synthesis -- Case Study 4: Defect Detection -- Numerical Lab 4: Working with Random Numbers -- Further Reading -- Sample Problems -- References -- 5 Feature Extraction and Selection -- 5.1 Fundamentals of Feature Extraction -- 5.2 Feature Extraction and Selection -- Case Study 5: Image Searching -- Numerical Lab 5: Extracting Features and Plotting Classes -- Further Reading -- Sample Problems -- References -- 6 Distance-Based Classification -- 6.1 Definitions of Distance -- 6.2 Class Prototype -- 6.3 Distance-Based Classification -- 6.4 Classifier Variations -- Case Study 6: Hand-writing Recognition -- Numerical Lab 6: Distance-Based Classifiers -- Further Reading -- Sample Problems -- References -- 7 Inferring Class Models -- 7.1 Parametric Estimation -- 7.2 Parametric Model Learning -- 7.3 Nonparametric Model Learning -- 7.3.1 Histogram Estimation -- 7.3.2 Kernel-Based Estimation -- 7.3.3 Neighbourhood-based Estimation -- 7.4 Distribution Assessment -- Case Study 7: Object Recognition -- Numerical Lab 7: Parametric and Nonparametric Estimation -- Further Reading -- Sample Problems -- References -- 8 Statistics-Based Classification -- 8.1 Non-Bayesian Classification: Maximum Likelihood.
8.2 Bayesian Classification: Maximum a Posteriori -- 8.3 Statistical Classification for Normal Distributions -- 8.4 Classification Error -- 8.5 Other Statistical Classifiers -- Case Study 8: Medical Assessments -- Numerical Lab 8: Statistical and Distance-Based Classifiers -- Further Reading -- Sample Problems -- References -- 9 Classifier Testing and Validation -- 9.1 Working with Data -- 9.2 Classifier Evaluation -- 9.3 Classifier Validation -- Case Study 9: Autonomous Vehicles -- Numerical Lab 9: Leave-One-Out Validation -- Further Reading -- Sample Problems -- References -- 10 Discriminant-Based Classification -- 10.1 Linear Discriminants -- 10.2 Discriminant Model Learning -- 10.3 Nonlinear Discriminants -- 10.4 Multi-Class Problems -- Case Study 10: Digital Communications -- Numerical Lab 10: Discriminants -- Further Reading -- Sample Problems -- References -- 11 Ensemble Classification -- 11.1 Combining Classifiers -- 11.2 Resampling Strategies -- 11.3 Sequential Strategies -- 11.4 Nonlinear Strategies -- 11.4.1 Neural Network Learning -- 11.4.2 Deep Neural Network Classifiers -- Case Study 11: Interpretability and Ethics of Large Networks -- Numerical Lab 11: Ensemble Classifiers -- Further Reading -- Sample Problems -- References -- 12 Model-Free Classification -- 12.1 Unsupervised Learning -- 12.1.1 K-Means Clustering -- 12.1.2 Kernel K-Means Clustering -- 12.1.3 Mean-Shift Clustering -- 12.1.4 Hierarchical Clustering -- 12.2 Network-Based Clustering -- 12.3 Semi-Supervised Learning -- Case Study 12: Ancient Text Analysis: Who Wrote What? -- Numerical Lab 12: Clustering -- Further Reading -- Sample Problems -- References -- 13 Conclusions and Directions -- Appendices -- A Algebra Review -- Further Reading -- Sample Problems -- References -- B Random Variables and Random Vectors -- B.1 Random Variables -- B.2 Expectations.
B.3 Conditional Statistics -- B.4 Random Vectors and Covariances -- B.5 Outliers and Heavy-Tail Distributions -- B.6 Sample Statistics -- Further Reading -- Sample Problems -- References -- C Introduction to Optimization -- C.1 Basic Principles -- C.2 One-Dimensional Optimization -- C.3 Multi-Dimensional Optimization -- C.4 Multi-Objective Optimization -- Further Reading -- Sample Problems -- References -- D Mathematical Derivations -- Index.
Record Nr. UNINA-9910629297203321
Fieguth Paul  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to pattern recognition and machine learning / / Paul Fieguth
An introduction to pattern recognition and machine learning / / Paul Fieguth
Autore Fieguth Paul
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (481 pages)
Disciplina 006.31
Soggetto topico Machine learning
Pattern perception
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 9783030959951
9783030959937
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Table of Contents -- List of Examples -- List of Algorithms -- Notation -- 1 Overview -- 2 Introduction to Pattern Recognition -- 2.1 What Is Pattern Recognition? -- 2.2 Measured Patterns -- 2.3 Classes -- 2.4 Classification -- 2.5 Types of Classification Problems -- Case Study 2: Biometrics -- Numerical Lab 2: The Iris Dataset -- Further Reading -- Sample Problems -- References -- 3 Learning -- Case Study 3: The Netflix Prize -- Numerical Lab 3: Overfitting and Underfitting -- Summary -- Further Reading -- Sample Problems -- References -- 4 Representing Patterns -- 4.1 Similarity -- 4.2 Class Shape -- 4.3 Cluster Synthesis -- Case Study 4: Defect Detection -- Numerical Lab 4: Working with Random Numbers -- Further Reading -- Sample Problems -- References -- 5 Feature Extraction and Selection -- 5.1 Fundamentals of Feature Extraction -- 5.2 Feature Extraction and Selection -- Case Study 5: Image Searching -- Numerical Lab 5: Extracting Features and Plotting Classes -- Further Reading -- Sample Problems -- References -- 6 Distance-Based Classification -- 6.1 Definitions of Distance -- 6.2 Class Prototype -- 6.3 Distance-Based Classification -- 6.4 Classifier Variations -- Case Study 6: Hand-writing Recognition -- Numerical Lab 6: Distance-Based Classifiers -- Further Reading -- Sample Problems -- References -- 7 Inferring Class Models -- 7.1 Parametric Estimation -- 7.2 Parametric Model Learning -- 7.3 Nonparametric Model Learning -- 7.3.1 Histogram Estimation -- 7.3.2 Kernel-Based Estimation -- 7.3.3 Neighbourhood-based Estimation -- 7.4 Distribution Assessment -- Case Study 7: Object Recognition -- Numerical Lab 7: Parametric and Nonparametric Estimation -- Further Reading -- Sample Problems -- References -- 8 Statistics-Based Classification -- 8.1 Non-Bayesian Classification: Maximum Likelihood.
8.2 Bayesian Classification: Maximum a Posteriori -- 8.3 Statistical Classification for Normal Distributions -- 8.4 Classification Error -- 8.5 Other Statistical Classifiers -- Case Study 8: Medical Assessments -- Numerical Lab 8: Statistical and Distance-Based Classifiers -- Further Reading -- Sample Problems -- References -- 9 Classifier Testing and Validation -- 9.1 Working with Data -- 9.2 Classifier Evaluation -- 9.3 Classifier Validation -- Case Study 9: Autonomous Vehicles -- Numerical Lab 9: Leave-One-Out Validation -- Further Reading -- Sample Problems -- References -- 10 Discriminant-Based Classification -- 10.1 Linear Discriminants -- 10.2 Discriminant Model Learning -- 10.3 Nonlinear Discriminants -- 10.4 Multi-Class Problems -- Case Study 10: Digital Communications -- Numerical Lab 10: Discriminants -- Further Reading -- Sample Problems -- References -- 11 Ensemble Classification -- 11.1 Combining Classifiers -- 11.2 Resampling Strategies -- 11.3 Sequential Strategies -- 11.4 Nonlinear Strategies -- 11.4.1 Neural Network Learning -- 11.4.2 Deep Neural Network Classifiers -- Case Study 11: Interpretability and Ethics of Large Networks -- Numerical Lab 11: Ensemble Classifiers -- Further Reading -- Sample Problems -- References -- 12 Model-Free Classification -- 12.1 Unsupervised Learning -- 12.1.1 K-Means Clustering -- 12.1.2 Kernel K-Means Clustering -- 12.1.3 Mean-Shift Clustering -- 12.1.4 Hierarchical Clustering -- 12.2 Network-Based Clustering -- 12.3 Semi-Supervised Learning -- Case Study 12: Ancient Text Analysis: Who Wrote What? -- Numerical Lab 12: Clustering -- Further Reading -- Sample Problems -- References -- 13 Conclusions and Directions -- Appendices -- A Algebra Review -- Further Reading -- Sample Problems -- References -- B Random Variables and Random Vectors -- B.1 Random Variables -- B.2 Expectations.
B.3 Conditional Statistics -- B.4 Random Vectors and Covariances -- B.5 Outliers and Heavy-Tail Distributions -- B.6 Sample Statistics -- Further Reading -- Sample Problems -- References -- C Introduction to Optimization -- C.1 Basic Principles -- C.2 One-Dimensional Optimization -- C.3 Multi-Dimensional Optimization -- C.4 Multi-Objective Optimization -- Further Reading -- Sample Problems -- References -- D Mathematical Derivations -- Index.
Record Nr. UNISA-996499871003316
Fieguth Paul  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Iterative learning control for nonlinear time-delay system / / Jianming Wei, Hong Wang, and Fang Liu
Iterative learning control for nonlinear time-delay system / / Jianming Wei, Hong Wang, and Fang Liu
Autore Wei Jianming
Pubbl/distr/stampa Singapore : , : Springer, , [2023]
Descrizione fisica 1 online resource (185 pages)
Disciplina 629.8
Soggetto topico Intelligent control systems
Machine learning
Control intel·ligent
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 981-19-6317-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- About This Book -- Contents -- 1 Introduction -- 1.1 Background -- 1.2 Research Status of ILC -- 1.2.1 Contraction Mapping Theorem Based Classical ILC -- 1.2.2 Composite Energy Function Based Adaptive ILC -- 1.2.3 2-D Theory Based ILC -- 1.3 Main Contents of the Book -- References -- 2 AILC of Parameterized Nonlinear Time-Delay Systems -- 2.1 Introduction -- 2.2 Problem Formulation and Preliminaries -- 2.2.1 Problem Formulation -- 2.2.2 Dead-Zone Characteristic -- 2.3 AILC Scheme Design -- 2.4 Stability Analysis -- 2.5 Simulation Analysis -- 2.5.1 Verification of the AILC Scheme -- 2.5.2 Comparison Simulation: Adaptive Control -- 2.6 Summary and Comments -- References -- 3 NN AILC of Nonlinear Time-Delay Systems -- 3.1 Introduction -- 3.2 Problem Formulation and Preliminaries -- 3.2.1 Problem Formulation -- 3.2.2 RBF Neural Network -- 3.3 RBF NN AILC Design -- 3.4 Stability Analysis -- 3.5 Simulation Analysis -- 3.5.1 Verification of the RBF NN AILC Scheme -- 3.5.2 Comparison Simulation: Adaptive NN Control -- 3.6 Summary and Comments -- References -- 4 AILC of Nonlinear Time-Delay Systems with Unknown Control Direction -- 4.1 Introduction -- 4.2 Problem Formulation and Preliminaries -- 4.2.1 Problem Formulation -- 4.2.2 Backlash-Like Hysteresis Nonlinearity -- 4.2.3 Nussbaum Gain Method -- 4.3 Nussbuam Gain-Based AILC Scheme Design -- 4.4 Stability Analysis -- 4.5 Simulation Analysis -- 4.5.1 Verification of Nussbuam Gain-Based AILC Scheme -- 4.5.2 Comparison Simulation: Nussbuam Gain-Based Adaptive NN Control -- 4.6 Summary and Comments -- References -- 5 Observer-Based AILC of Nonlinear Time-Delay Systems -- 5.1 Introduction -- 5.2 Problem Formulation and Preliminaries -- 5.2.1 Problem Formulation -- 5.2.2 Input Saturation Nonlinearity -- 5.2.3 Schur Complementary Lemma.
5.3 State Observer-Based AILC Design and Stability Analysis -- 5.3.1 State Observer Design -- 5.3.2 NN AILC Scheme Design -- 5.3.3 Stability Analysis -- 5.3.4 Simulation Analysis -- 5.4 Error Observer-Based AILC Design and Stability Analysis -- 5.4.1 Error Observer-Based AILC Scheme Design -- 5.4.2 Stability Analysis -- 5.4.3 Simulation Analysis -- 5.5 Summary and Comments -- References -- 6 Observer-Based AILC Design for Robotic Manipulator -- 6.1 Introduction -- 6.2 Problem Formulation and Preliminaries -- 6.2.1 Problem Formulation -- 6.2.2 'GL' Matrix and Operators -- 6.3 States Observer Design -- 6.4 AILC Design -- 6.5 Simulation Analysis -- 6.6 Summary and Comments -- References.
Record Nr. UNISA-996503548903316
Wei Jianming  
Singapore : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui