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An introduction to complex systems : society, ecology, and nonlinear dynamics / / Paul Fieguth
| An introduction to complex systems : society, ecology, and nonlinear dynamics / / Paul Fieguth |
| Autore | Fieguth Paul |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (465 pages) |
| Disciplina | 531 |
| Soggetto topico | Nonlinear mechanics |
| ISBN | 3-030-63168-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910497086303321 |
Fieguth Paul
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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An introduction to complex systems : society, ecology, and nonlinear dynamics / / Paul Fieguth
| An introduction to complex systems : society, ecology, and nonlinear dynamics / / Paul Fieguth |
| Autore | Fieguth Paul |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (465 pages) |
| Disciplina | 531 |
| Soggetto topico | Nonlinear mechanics |
| ISBN | 3-030-63168-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466848903316 |
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Fieguth Paul
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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An Introduction to Complex Systems : Society, Ecology, and Nonlinear Dynamics / / by Paul Fieguth
| An Introduction to Complex Systems : Society, Ecology, and Nonlinear Dynamics / / by Paul Fieguth |
| Autore | Fieguth Paul |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XII, 346 p. 243 illus., 178 illus. in color.) |
| Disciplina | 531 |
| Soggetto topico |
Statistical physics
Dynamical systems Computational complexity System theory Physical geography Climate change Game theory Complex Systems Complexity Earth System Sciences Climate Change Game Theory |
| ISBN | 3-319-44606-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 2 Global Warming and Climate Change -- Further Reading -- 3 Systems Theory -- 3.1 Systems & Boundaries -- 3.2 Systems & Thermodynamics.-3.3 Systems of Systems -- Case Study 3: Nutrient Flows, Irrigation, and Desertification -- Further Reading -- Sample Problems -- 4 Dynamic Systems -- 4.1 System State -- 4.2 Randomness -- 4.3 Analysis -- 4.3.1 Correlation -- 4.3.2 Stationarity -- 4.3.3 Transformations -- Case Study 4: Water Levels of the Oceans and Great Lakes -- Further Reading -- Sample Problems -- 5 Linear Systems -- 5.1 Linearity -- 5.2 Modes -- 5.3 System Coupling -- 5.4 Dynamics -- 5.5 Non-Normal Systems -- Case Study 5: System Decoupling -- Further Reading -- Sample Problems -- 6 Nonlinear Dynamic Systems – Uncoupled -- 6.1 Simple Dynamics -- 6.2 Bifurcations -- 6.3 Hysteresis and Catastrophes -- 6.4 System Behaviour near Folds -- 6.5 Overview -- Case Study 6: Climate and Hysteresis -- Further Reading -- Sample Problems -- 7 Nonlinear Dynamic Systems – Coupled.-7.1 Linearization -- 7.2 2D Nonlinear Systems -- 7.3 Limit Cycles and Bifurcations -- Case Study 7: Geysers, Earthquakes, and Limit Cycles -- Further Reading -- Sample Problems -- 8 Spatial Systems -- 8.1 PDEs -- 8.2 PDEs & Earth Systems -- 8.3 Discretization -- 8.4 Spatial Continuous-State Models -- 8.5 Spatial Discrete-State Models -- 8.6 Agent Models -- Case Study 8: Global circulation models -- Further Reading -- Sample Problems -- 9 Power Laws and Non-Gaussian Systems -- 9.1 The Gaussian Distribution 9.2 The Exponential Distribution -- 9.3 Heavy Tailed Distributions -- 9.4 Sources of Power Laws -- 9.5 Synthesis and Analysis of Power Laws -- Case Study 9: Power Laws in Social Systems -- Further Reading -- Sample Problems -- 10 Complex Systems -- 10.1 Spatial Nonlinear Models -- 10.2 Self-Organized Criticality -- 10.3 Emergence -- 10.4 Complex Systems of Systems -- Case Study 10: Complex Systems in Nature -- Further Reading -- Sample Problems -- 11 Observation & Inference -- 11.1 Forward Models -- 11.2 Remote Measurement -- 11.3 Resolution.-11.4 Inverse Problems -- Case Study 11A: Sensing— Synthetic Aperture Radar -- Case Study 11B: Inversion— Atmospheric Temperature -- Further Reading -- Sample Problems -- 12 Water.-12.1 Ocean Acidification -- 12.2 Ocean Garbage -- 12.3 Groundwater -- Case Study 12: Satellite Remote Sensing of the Ocean -- Further Reading -- Sample Problems -- 13 Concluding Thoughts -- Further Reading -- Part I Appendices -- Index. |
| Record Nr. | UNINA-9910254585503321 |
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Fieguth Paul
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Fieguth Paul
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||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
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Fieguth Paul
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||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||