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Representation learning : propositionalization and embeddings / / Nada Lavrac, Vid Podpecan, Marko Robnik-Sikonja
Representation learning : propositionalization and embeddings / / Nada Lavrac, Vid Podpecan, Marko Robnik-Sikonja
Autore Lavrač Nada
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , 2021
Descrizione fisica 1 online resource (175 pages)
Disciplina 006.31
Soggetto topico Machine learning
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 3-030-68817-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Representation Learning -- 1.1 Motivation -- 1.2 Representation Learning in Knowledge Discovery -- 1.2.1 Machine Learning and Knowledge Discovery -- 1.2.2 Automated Data Transformation -- 1.3 Data Transformations and Information Representation Levels -- 1.3.1 Information Representation Levels -- 1.3.2 Propositionalization: Learning Symbolic Vector Representations -- 1.3.3 Embeddings: Learning Numeric Vector Representations -- 1.4 Evaluation of Propositionalization and Embeddings -- 1.4.1 Performance Evaluation -- 1.4.2 Interpretability -- 1.5 Survey of Automated Data Transformation Methods -- 1.6 Outline of This Monograph -- References -- 2 Machine Learning Background -- 2.1 Machine Learning -- 2.1.1 Attributes and Features -- 2.1.2 Machine Learning Approaches -- 2.1.3 Decision and Regression Tree Learning -- 2.1.4 Rule Learning -- 2.1.5 Kernel Methods -- 2.1.6 Ensemble Methods -- 2.1.7 Deep Neural Networks -- 2.2 Text Mining -- 2.3 Relational Learning -- 2.4 Network Analysis -- 2.4.1 Selected Homogeneous Network Analysis Tasks -- 2.4.2 Selected Heterogeneous Network Analysis Tasks -- 2.4.3 Semantic Data Mining -- 2.4.4 Network Representation Learning -- 2.5 Evaluation -- 2.5.1 Classifier Evaluation Measures -- 2.5.2 Rule Evaluation Measures -- 2.6 Data Mining and Selected Data Mining Platforms -- 2.6.1 Data Mining -- 2.6.2 Selected Data Mining Platforms -- 2.7 Implementation and Reuse -- References -- 3 Text Embeddings -- 3.1 Background Technologies -- 3.1.1 Transfer Learning -- 3.1.2 Language Models -- 3.2 Word Cooccurrence-Based Embeddings -- 3.2.1 Sparse Word Cooccurrence-Based Embeddings -- 3.2.2 Weighting Schemes -- 3.2.3 Similarity Measures -- 3.2.4 Sparse Matrix Representations of Texts -- 3.2.5 Dense Term-Matrix Based Word Embeddings -- 3.2.6 Dense Topic-Based Embeddings.
3.3 Neural Word Embeddings -- 3.3.1 Word2vec Embeddings -- 3.3.2 GloVe Embeddings -- 3.3.3 Contextual Word Embeddings -- 3.4 Sentence and Document Embeddings -- 3.5 Cross-Lingual Embeddings -- 3.6 Intrinsic Evaluation of Text Embeddings -- 3.7 Implementation and Reuse -- 3.7.1 LSA and LDA -- 3.7.2 word2vec -- 3.7.3 BERT -- References -- 4 Propositionalization of Relational Data -- 4.1 Relational Learning -- 4.2 Relational Data Representation -- 4.2.1 Illustrative Example -- 4.2.2 Example Using a Logical Representation -- 4.2.3 Example Using a Relational Database Representation -- 4.3 Propositionalization -- 4.3.1 Relational Features -- 4.3.2 Automated Construction of Relational Features by RSD -- 4.3.3 Automated Data Transformation and Learning -- 4.4 Selected Propositionalization Approaches -- 4.5 Wordification: Unfolding Relational Data into BoW Vectors -- 4.5.1 Outline of the Wordification Approach -- 4.5.2 Wordification Algorithm -- 4.5.3 Improved Efficiency of Wordification Algorithm -- 4.6 Deep Relational Machines -- 4.7 Implementation and Reuse -- 4.7.1 Wordification -- 4.7.2 Python-rdm Package -- References -- 5 Graph and Heterogeneous Network Transformations -- 5.1 Embedding Simple Graphs -- 5.1.1 DeepWalk Algorithm -- 5.1.2 Node2vec Algorithm -- 5.1.3 Other Random Walk-Based Graph Embedding Algorithms -- 5.2 Embedding Heterogeneous Information Networks -- 5.2.1 Heterogeneous Information Networks -- 5.2.2 Examples of Heterogeneous Information Networks -- 5.2.3 Embedding Feature-Rich Graphs with GCNs -- 5.2.4 Other Heterogeneous Network Embedding Approaches -- 5.3 Propositionalizing Heterogeneous Information Networks -- 5.3.1 TEHmINe Propositionalization of Text-Enriched Networks -- 5.3.1.1 Heterogeneous Network Decomposition -- 5.3.1.2 Feature Vector Construction -- 5.3.1.3 Data Fusion -- 5.3.2 HINMINE Heterogeneous Networks Decomposition.
5.4 Ontology Transformations -- 5.4.1 Ontologies and Semantic Data Mining -- 5.4.2 NetSDM Ontology Reduction Methodology -- 5.4.2.1 Converting Ontology and Examples into Network Format -- 5.4.2.2 Term Significance Calculation -- 5.4.2.3 Network Node Removal -- 5.5 Embedding Knowledge Graphs -- 5.6 Implementation and Reuse -- 5.6.1 Node2vec -- 5.6.2 Metapath2vec -- 5.6.3 HINMINE -- References -- 6 Unified Representation Learning Approaches -- 6.1 Entity Embeddings with StarSpace -- 6.2 Unified Approaches for Relational Data -- 6.2.1 PropStar: Feature-Based Relational Embeddings -- 6.2.2 PropDRM: Instance-Based Relational Embeddings -- 6.2.3 Performance Evaluation of Relational Embeddings -- 6.3 Implementation and Reuse -- 6.3.1 StarSpace -- 6.3.2 PropDRM -- References -- 7 Many Faces of Representation Learning -- 7.1 Unifying Aspects in Terms of Data Representation -- 7.2 Unifying Aspects in Terms of Learning -- 7.3 Unifying Aspects in Terms of Use -- 7.4 Summary and Conclusions -- References -- Index.
Record Nr. UNISA-996466391103316
Lavrač Nada  
Cham, Switzerland : , : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges [[electronic resource] ] : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers / / edited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges [[electronic resource] ] : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers / / edited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XV, 417 p. 176 illus., 165 illus. in color.)
Disciplina 621.367
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Computer vision
Machine learning
Pattern recognition systems
Social sciences - Data processing
Education - Data processing
Computer Vision
Machine Learning
Automated Pattern Recognition
Computer Application in Social and Behavioral Sciences
Computers and Education
Aprenentatge automàtic
Intel·ligència artificial
Imatges per ressonància magnètica
Malalties cardiovasculars
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-68107-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Regular papers -- A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI -- Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view -- Graph convolutional regression of cardiac depolarization from sparse endocardial maps -- A cartesian grid representation of left atrial appendages for deep learning based estimation of thrombogenic risk predictors -- Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks -- Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions- Towards mesh-free patient-specific mitral valve modeling -- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps -- Automatic Detection of Landmarks for Fast Cardiac MR Image Registration -- Quality-aware semi-supervised learning for CMR segmentation -- Estimation of imaging biomarker’s progression in post-infarct patients using cross-sectional data -- PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data -- Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters -- Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI -- Estimation of Cardiac Valve Annuli Motion with Deep Learning -- 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration -- Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning -- M&Ms challenge -- Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation -- Disentangled Representations for Domain-generalized Cardiac Segmentation -- A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation -- Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information -- Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer -- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation -- Studying Robustness of Segmantic Segmentation under Domain Shift in cardiac MRI -- A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences -- Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET -- Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation -- Deidentifying MRI data domain by iterative backpropagation -- A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net -- Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation -- Style-invariant Cardiac Image Segmentation with Test-time Augmentation -- EMIDEC challenge -- Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI -- Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI -- Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information -- SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection -- A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI -- Efficient 3D deep learning for myocardial diseases segmentation -- Deep-learning-based myocardial pathology detection -- Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks -- Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR images -- Anatomy Prior Based U-net for Pathology Segmentation with Attention -- Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-based Augmentation -- Classification of pathological cases of myocardial infarction using Convolutional Neural Network and Random Forest. .
Record Nr. UNISA-996464521503316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers / / edited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers / / edited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XV, 417 p. 176 illus., 165 illus. in color.)
Disciplina 621.367
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Computer vision
Machine learning
Pattern recognition systems
Social sciences - Data processing
Education - Data processing
Computer Vision
Machine Learning
Automated Pattern Recognition
Computer Application in Social and Behavioral Sciences
Computers and Education
Aprenentatge automàtic
Intel·ligència artificial
Imatges per ressonància magnètica
Malalties cardiovasculars
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-68107-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Regular papers -- A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI -- Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view -- Graph convolutional regression of cardiac depolarization from sparse endocardial maps -- A cartesian grid representation of left atrial appendages for deep learning based estimation of thrombogenic risk predictors -- Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks -- Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions- Towards mesh-free patient-specific mitral valve modeling -- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps -- Automatic Detection of Landmarks for Fast Cardiac MR Image Registration -- Quality-aware semi-supervised learning for CMR segmentation -- Estimation of imaging biomarker’s progression in post-infarct patients using cross-sectional data -- PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data -- Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters -- Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI -- Estimation of Cardiac Valve Annuli Motion with Deep Learning -- 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration -- Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning -- M&Ms challenge -- Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation -- Disentangled Representations for Domain-generalized Cardiac Segmentation -- A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation -- Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information -- Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer -- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation -- Studying Robustness of Segmantic Segmentation under Domain Shift in cardiac MRI -- A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences -- Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET -- Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation -- Deidentifying MRI data domain by iterative backpropagation -- A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net -- Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation -- Style-invariant Cardiac Image Segmentation with Test-time Augmentation -- EMIDEC challenge -- Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI -- Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI -- Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information -- SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection -- A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI -- Efficient 3D deep learning for myocardial diseases segmentation -- Deep-learning-based myocardial pathology detection -- Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks -- Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR images -- Anatomy Prior Based U-net for Pathology Segmentation with Attention -- Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-based Augmentation -- Classification of pathological cases of myocardial infarction using Convolutional Neural Network and Random Forest. .
Record Nr. UNINA-9910483725503321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical inference and machine learning for big data / / Mayer Alvo
Statistical inference and machine learning for big data / / Mayer Alvo
Autore Alvo Mayer
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (442 pages)
Disciplina 005.7
Collana Springer series in the data sciences
Soggetto topico Big data
Machine learning
Mathematical statistics
Dades massives
Aprenentatge automàtic
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783031067846
9783031067839
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- List of Acronyms -- List of Nomenclatures -- List of Figures -- List of Tables -- I. Introduction to Big Data -- 1. Examples of Big Data -- 1.1. Multivariate Data -- 1.2. Categorical Data -- 1.3. Environmental Data -- 1.4. Genetic Data -- 1.5. Time Series Data -- 1.6. Ranking Data -- 1.7. Social Network Data -- 1.8. Symbolic Data -- 1.9. Image Data -- II. Statistical Inference for Big Data -- 2. Basic Concepts in Probability -- 2.1. Pearson System of Distributions -- 2.2. Modes of Convergence -- 2.3. Multivariate Central Limit Theorem -- 2.4. Markov Chains -- 3. Basic Concepts in Statistics -- 3.1. Parametric Estimation -- 3.2. Hypothesis Testing -- 3.3. Classical Bayesian Statistics -- 4. Multivariate Methods -- 4.1. Matrix Algebra -- 4.2. Multivariate Analysis as a Generalization of Univariate Analysis -- 4.2.1. The General Linear Model -- 4.2.2. One Sample Problem -- 4.2.3. Two-Sample Problem -- 4.3. Structure in Multivariate Data Analysis -- 4.3.1. Principal Component Analysis -- 4.3.2. Factor Analysis -- 4.3.3. Canonical Correlation -- 4.3.4. Linear Discriminant Analysis -- 4.3.5. Multidimensional Scaling -- 4.3.6. Copula Methods -- 5. Nonparametric Statistics -- 5.1. Goodness-of-Fit Tests -- 5.2. Linear Rank Statistics -- 5.3. U Statistics -- 5.4. Hoeffding's Combinatorial Central Limit Theorem -- 5.5. Nonparametric Tests -- 5.5.1. One-Sample Tests of Location -- 5.5.2. Confidence Interval for the Median -- 5.5.3. Wilcoxon Signed Rank Test -- 5.6. Multi-Sample Tests -- 5.6.1. Two-Sample Tests for Location -- 5.6.2. Multi-Sample Test for Location -- 5.6.3. Tests for Dispersion -- 5.7. Compatibility -- 5.8. Tests for Ordered Alternatives -- 5.9. A Unified Theory of Hypothesis Testing -- 5.9.1. Umbrella Alternatives -- 5.9.2. Tests for Trend in Proportions -- 5.10. Randomized Block Designs.
5.11. Density Estimation -- 5.11.1. Univariate Kernel Density Estimation -- 5.11.2. The Rank Transform -- 5.11.3. Multivariate Kernel Density Estimation -- 5.12. Spatial Data Analysis -- 5.12.1. Spatial Prediction -- 5.12.2. Point Poisson Kriging of Areal Data -- 5.13. Efficiency -- 5.13.1. Pitman Efficiency -- 5.13.2. Application of Le Cam's Lemmas -- 5.14. Permutation Methods -- 6. Exponential Tilting and Its Applications -- 6.1. Neyman Smooth Tests -- 6.2. Smooth Models for Discrete Distributions -- 6.3. Rejection Sampling -- 6.4. Tweedie's Formula: Univariate Case -- 6.5. Tweedie's Formula: Multivariate Case -- 6.6. The Saddlepoint Approximation and Notions of Information -- 7. Counting Data Analysis -- 7.1. Inference for Generalized Linear Models -- 7.2. Inference for Contingency Tables -- 7.3. Two-Way Ordered Classifications -- 7.4. Survival Analysis -- 7.4.1. Kaplan-Meier Estimator -- 7.4.2. Modeling Survival Data -- 8. Time Series Methods -- 8.1. Classical Methods of Analysis -- 8.2. State Space Modeling -- 9. Estimating Equations -- 9.1. Composite Likelihood -- 9.2. Empirical Likelihood -- 9.2.1. Application to One-Sample Ranking Problems -- 9.2.2. Application to Two-Sample Ranking Problems -- 10. Symbolic Data Analysis -- 10.1. Introduction -- 10.2. Some Examples -- 10.3. Interval Data -- 10.3.1. Frequency -- 10.3.2. Sample Mean and Sample Variance -- 10.3.3. Realization In SODAS -- 10.4. Multi-nominal Data -- 10.4.1. Frequency -- 10.5. Symbolic Regression -- 10.5.1. Symbolic Regression for Interval Data -- 10.5.2. Symbolic Regression for Modal Data -- 10.5.3. Symbolic Regression in SODAS -- 10.6. Cluster Analysis -- 10.7. Factor Analysis -- 10.8. Factorial Discriminant Analysis -- 10.9. Application to Parkinson's Disease -- 10.9.1. Data Processing -- 10.9.2. Result Analysis -- 10.9.2.1. Viewer -- 10.9.2.2. Descriptive Statistics.
10.9.2.3. Symbolic Regression Analysis -- 10.9.2.4. Symbolic Clustering -- 10.9.2.5. Principal Component Analysis -- 10.9.3. Comparison with Classical Method -- 10.10. Application to Cardiovascular Disease Analysis -- 10.10.1. Results of the Analysis -- 10.10.2. Comparison with the Classical Method -- III. Machine Learning for Big Data -- 11. Tools for Machine Learning -- 11.1. Regression Models -- 11.2. Simple Linear Regression -- 11.2.1. Least Squares Method -- 11.2.2. Statistical Inference on Regression Coefficients -- 11.2.3. Verifying the Assumptions on the Error Terms -- 11.3. Multiple Linear Regression -- 11.3.1. Multiple Linear Regression Model -- 11.3.2. Normal Equations -- 11.3.3. Statistical Inference on Regression Coefficients -- 11.3.4. Model Fit Evaluation -- 11.4. Regression in Machine Learning -- 11.4.1. Optimization for Linear Regression in Machine Learning -- 11.4.1.1. Gradient Descent -- 11.4.1.2. Feature Standardization -- 11.4.1.3. Computing Cost on a Test Set -- 11.5. Classification Models -- 11.5.1. Logistic Regression -- 11.5.1.1. Optimization with Maximal Likelihood for Logistic Regression -- 11.5.1.2. Statistical Inference -- 11.5.2. Logistic Regression for Binary Classification -- 11.5.2.1. Kullback-Leibler Divergence -- 11.5.3. Logistic Regression with Multiple Response Classes -- 11.5.4. Regularization for Regression Models in Machine Learning -- 11.5.4.1. Ridge Regression -- 11.5.4.2. Lasso Regression -- 11.5.4.3. The Choice of Regularization Method -- 11.5.5. Support Vector Machines (SVM) -- 11.5.5.1. Introduction -- 11.5.5.2. Finding the Optimal Hyperplane -- 11.5.5.3. SVM for Nonlinearly Separable Data Sets -- 11.5.5.4. Illustrating SVM -- 12. Neural Networks -- 12.1. Feed-Forward Networks -- 12.1.1. Motivation -- 12.1.2. Introduction to Neural Networks -- 12.1.3. Building a Deep Feed-Forward Network.
12.1.4. Learning in Deep Networks -- 12.1.4.1. Quantitative Model -- 12.1.4.2. Binary Classification Model -- 12.1.5. Generalization -- 12.1.5.1. A Machine Learning Approach to Generalization -- 12.2. Recurrent Neural Networks -- 12.2.1. Building a Recurrent Neural Network -- 12.2.2. Learning in Recurrent Networks -- 12.2.3. Most Common Design Structures of RNNs -- 12.2.4. Deep RNN -- 12.2.5. Bidirectional RNN -- 12.2.6. Long-Term Dependencies and LSTM RNN -- 12.2.7. Reduction for Exploding Gradients -- 12.3. Convolution Neural Networks -- 12.3.1. Convolution Operator for Arrays -- 12.3.1.1. Properties of the Convolution Operator -- 12.3.2. Convolution Layers -- 12.3.3. Pooling Layers -- 12.4. Text Analytics -- 12.4.1. Introduction -- 12.4.2. General Architecture -- IV. Computational Methods for Statistical Inference -- 13. Bayesian Computation Methods -- 13.1. Data Augmentation Methods -- 13.2. Metropolis-Hastings Algorithm -- 13.3. Gibbs Sampling -- 13.4. EM Algorithm -- 13.4.1. Application to Ranking -- 13.4.2. Extension to Several Populations -- 13.5. Variational Bayesian Methods -- 13.5.1. Optimization of the Variational Distribution -- 13.6. Bayesian Nonparametric Methods -- 13.6.1. Dirichlet Prior -- 13.6.2. The Poisson-Dirichlet Prior -- 13.6.3. Simulation of Bayesian Posterior Distributions -- 13.6.4. Other Applications -- Bibliography -- Index.
Record Nr. UNINA-9910633928903321
Alvo Mayer  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical inference and machine learning for big data / / Mayer Alvo
Statistical inference and machine learning for big data / / Mayer Alvo
Autore Alvo Mayer
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (442 pages)
Disciplina 005.7
Collana Springer series in the data sciences
Soggetto topico Big data
Machine learning
Mathematical statistics
Dades massives
Aprenentatge automàtic
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783031067846
9783031067839
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- List of Acronyms -- List of Nomenclatures -- List of Figures -- List of Tables -- I. Introduction to Big Data -- 1. Examples of Big Data -- 1.1. Multivariate Data -- 1.2. Categorical Data -- 1.3. Environmental Data -- 1.4. Genetic Data -- 1.5. Time Series Data -- 1.6. Ranking Data -- 1.7. Social Network Data -- 1.8. Symbolic Data -- 1.9. Image Data -- II. Statistical Inference for Big Data -- 2. Basic Concepts in Probability -- 2.1. Pearson System of Distributions -- 2.2. Modes of Convergence -- 2.3. Multivariate Central Limit Theorem -- 2.4. Markov Chains -- 3. Basic Concepts in Statistics -- 3.1. Parametric Estimation -- 3.2. Hypothesis Testing -- 3.3. Classical Bayesian Statistics -- 4. Multivariate Methods -- 4.1. Matrix Algebra -- 4.2. Multivariate Analysis as a Generalization of Univariate Analysis -- 4.2.1. The General Linear Model -- 4.2.2. One Sample Problem -- 4.2.3. Two-Sample Problem -- 4.3. Structure in Multivariate Data Analysis -- 4.3.1. Principal Component Analysis -- 4.3.2. Factor Analysis -- 4.3.3. Canonical Correlation -- 4.3.4. Linear Discriminant Analysis -- 4.3.5. Multidimensional Scaling -- 4.3.6. Copula Methods -- 5. Nonparametric Statistics -- 5.1. Goodness-of-Fit Tests -- 5.2. Linear Rank Statistics -- 5.3. U Statistics -- 5.4. Hoeffding's Combinatorial Central Limit Theorem -- 5.5. Nonparametric Tests -- 5.5.1. One-Sample Tests of Location -- 5.5.2. Confidence Interval for the Median -- 5.5.3. Wilcoxon Signed Rank Test -- 5.6. Multi-Sample Tests -- 5.6.1. Two-Sample Tests for Location -- 5.6.2. Multi-Sample Test for Location -- 5.6.3. Tests for Dispersion -- 5.7. Compatibility -- 5.8. Tests for Ordered Alternatives -- 5.9. A Unified Theory of Hypothesis Testing -- 5.9.1. Umbrella Alternatives -- 5.9.2. Tests for Trend in Proportions -- 5.10. Randomized Block Designs.
5.11. Density Estimation -- 5.11.1. Univariate Kernel Density Estimation -- 5.11.2. The Rank Transform -- 5.11.3. Multivariate Kernel Density Estimation -- 5.12. Spatial Data Analysis -- 5.12.1. Spatial Prediction -- 5.12.2. Point Poisson Kriging of Areal Data -- 5.13. Efficiency -- 5.13.1. Pitman Efficiency -- 5.13.2. Application of Le Cam's Lemmas -- 5.14. Permutation Methods -- 6. Exponential Tilting and Its Applications -- 6.1. Neyman Smooth Tests -- 6.2. Smooth Models for Discrete Distributions -- 6.3. Rejection Sampling -- 6.4. Tweedie's Formula: Univariate Case -- 6.5. Tweedie's Formula: Multivariate Case -- 6.6. The Saddlepoint Approximation and Notions of Information -- 7. Counting Data Analysis -- 7.1. Inference for Generalized Linear Models -- 7.2. Inference for Contingency Tables -- 7.3. Two-Way Ordered Classifications -- 7.4. Survival Analysis -- 7.4.1. Kaplan-Meier Estimator -- 7.4.2. Modeling Survival Data -- 8. Time Series Methods -- 8.1. Classical Methods of Analysis -- 8.2. State Space Modeling -- 9. Estimating Equations -- 9.1. Composite Likelihood -- 9.2. Empirical Likelihood -- 9.2.1. Application to One-Sample Ranking Problems -- 9.2.2. Application to Two-Sample Ranking Problems -- 10. Symbolic Data Analysis -- 10.1. Introduction -- 10.2. Some Examples -- 10.3. Interval Data -- 10.3.1. Frequency -- 10.3.2. Sample Mean and Sample Variance -- 10.3.3. Realization In SODAS -- 10.4. Multi-nominal Data -- 10.4.1. Frequency -- 10.5. Symbolic Regression -- 10.5.1. Symbolic Regression for Interval Data -- 10.5.2. Symbolic Regression for Modal Data -- 10.5.3. Symbolic Regression in SODAS -- 10.6. Cluster Analysis -- 10.7. Factor Analysis -- 10.8. Factorial Discriminant Analysis -- 10.9. Application to Parkinson's Disease -- 10.9.1. Data Processing -- 10.9.2. Result Analysis -- 10.9.2.1. Viewer -- 10.9.2.2. Descriptive Statistics.
10.9.2.3. Symbolic Regression Analysis -- 10.9.2.4. Symbolic Clustering -- 10.9.2.5. Principal Component Analysis -- 10.9.3. Comparison with Classical Method -- 10.10. Application to Cardiovascular Disease Analysis -- 10.10.1. Results of the Analysis -- 10.10.2. Comparison with the Classical Method -- III. Machine Learning for Big Data -- 11. Tools for Machine Learning -- 11.1. Regression Models -- 11.2. Simple Linear Regression -- 11.2.1. Least Squares Method -- 11.2.2. Statistical Inference on Regression Coefficients -- 11.2.3. Verifying the Assumptions on the Error Terms -- 11.3. Multiple Linear Regression -- 11.3.1. Multiple Linear Regression Model -- 11.3.2. Normal Equations -- 11.3.3. Statistical Inference on Regression Coefficients -- 11.3.4. Model Fit Evaluation -- 11.4. Regression in Machine Learning -- 11.4.1. Optimization for Linear Regression in Machine Learning -- 11.4.1.1. Gradient Descent -- 11.4.1.2. Feature Standardization -- 11.4.1.3. Computing Cost on a Test Set -- 11.5. Classification Models -- 11.5.1. Logistic Regression -- 11.5.1.1. Optimization with Maximal Likelihood for Logistic Regression -- 11.5.1.2. Statistical Inference -- 11.5.2. Logistic Regression for Binary Classification -- 11.5.2.1. Kullback-Leibler Divergence -- 11.5.3. Logistic Regression with Multiple Response Classes -- 11.5.4. Regularization for Regression Models in Machine Learning -- 11.5.4.1. Ridge Regression -- 11.5.4.2. Lasso Regression -- 11.5.4.3. The Choice of Regularization Method -- 11.5.5. Support Vector Machines (SVM) -- 11.5.5.1. Introduction -- 11.5.5.2. Finding the Optimal Hyperplane -- 11.5.5.3. SVM for Nonlinearly Separable Data Sets -- 11.5.5.4. Illustrating SVM -- 12. Neural Networks -- 12.1. Feed-Forward Networks -- 12.1.1. Motivation -- 12.1.2. Introduction to Neural Networks -- 12.1.3. Building a Deep Feed-Forward Network.
12.1.4. Learning in Deep Networks -- 12.1.4.1. Quantitative Model -- 12.1.4.2. Binary Classification Model -- 12.1.5. Generalization -- 12.1.5.1. A Machine Learning Approach to Generalization -- 12.2. Recurrent Neural Networks -- 12.2.1. Building a Recurrent Neural Network -- 12.2.2. Learning in Recurrent Networks -- 12.2.3. Most Common Design Structures of RNNs -- 12.2.4. Deep RNN -- 12.2.5. Bidirectional RNN -- 12.2.6. Long-Term Dependencies and LSTM RNN -- 12.2.7. Reduction for Exploding Gradients -- 12.3. Convolution Neural Networks -- 12.3.1. Convolution Operator for Arrays -- 12.3.1.1. Properties of the Convolution Operator -- 12.3.2. Convolution Layers -- 12.3.3. Pooling Layers -- 12.4. Text Analytics -- 12.4.1. Introduction -- 12.4.2. General Architecture -- IV. Computational Methods for Statistical Inference -- 13. Bayesian Computation Methods -- 13.1. Data Augmentation Methods -- 13.2. Metropolis-Hastings Algorithm -- 13.3. Gibbs Sampling -- 13.4. EM Algorithm -- 13.4.1. Application to Ranking -- 13.4.2. Extension to Several Populations -- 13.5. Variational Bayesian Methods -- 13.5.1. Optimization of the Variational Distribution -- 13.6. Bayesian Nonparametric Methods -- 13.6.1. Dirichlet Prior -- 13.6.2. The Poisson-Dirichlet Prior -- 13.6.3. Simulation of Bayesian Posterior Distributions -- 13.6.4. Other Applications -- Bibliography -- Index.
Record Nr. UNISA-996499866303316
Alvo Mayer  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Statistics with Julia : fundamentals for data science, machine learning and artificial intelligence / / Yoni Nazarathy, Hayden Klok
Statistics with Julia : fundamentals for data science, machine learning and artificial intelligence / / Yoni Nazarathy, Hayden Klok
Autore Nazarathy Yoni
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (531 pages)
Disciplina 519.2
Collana Springer Series in the Data Sciences
Soggetto topico Probabilities - Data processing
Statistics - Data processing
Estadística
Estructures de dades (Informàtica)
Estadística matemàtica
Probabilitats - Informàtica
Estadística - Informàtica
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 9783030709013
3-030-70901-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models
Record Nr. UNINA-9910497089603321
Nazarathy Yoni  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Statistics with Julia : fundamentals for data science, machine learning and artificial intelligence / / Yoni Nazarathy, Hayden Klok
Statistics with Julia : fundamentals for data science, machine learning and artificial intelligence / / Yoni Nazarathy, Hayden Klok
Autore Nazarathy Yoni
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (531 pages)
Disciplina 519.2
Collana Springer Series in the Data Sciences
Soggetto topico Probabilities - Data processing
Statistics - Data processing
Estadística
Estructures de dades (Informàtica)
Estadística matemàtica
Probabilitats - Informàtica
Estadística - Informàtica
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 9783030709013
3-030-70901-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models
Record Nr. UNISA-996466408803316
Nazarathy Yoni  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Sustainable Statistical and Data Science Methods and Practices [[electronic resource] ] : Reports from LISA 2020 Global Network, Ghana, 2022 / / edited by O. Olawale Awe, Eric A. Vance
Sustainable Statistical and Data Science Methods and Practices [[electronic resource] ] : Reports from LISA 2020 Global Network, Ghana, 2022 / / edited by O. Olawale Awe, Eric A. Vance
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (XXIV, 415 p. 150 illus., 127 illus. in color.)
Disciplina 005.7
Collana STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health
Soggetto topico Artificial intelligence - Data processing
Data mining
Machine learning
Data Science
Data Mining and Knowledge Discovery
Statistical Learning
Aprenentatge automàtic
Estadística
Desenvolupament sostenible
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-41352-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter. 1. Using social media and network services to promote statistical collaboration laboratories: A case study of LEA Brazil -- Chapter. 2. Renewable Energy Forecasting Using Deep Learning Models -- Chapter. 3. Exploring feature selection and supervised classification algorithms for predicting Obesity among rural women for policy decisions -- Chapter. 4. Re-examining Inflation and its drivers in Nigeria: A machine learning approach -- Chapter. 5. Estimating Relative Response Rates and Preferential Ranking of Subjects -- Chapter. 6. Wealth Creation and Poverty Alleviation in a Nigerian State: A Recent Evidence-Based Survey -- Chapter. 7. Effect of Statistics on Collaboration for Enhancing Institutional Sustainability: A Case of Mzumbe University-Tanzania -- Chapter. 8. Strategies for the Sustainability of Stat Labs: A Case Study of Laboratory of Interdisciplinary Statistical Analysis, Lahore College for Women University Lahore, Pakistan (LISA-LCWU) -- Chapter. 9. Advanced Mathematics and Computations for Innovation and Sustainability of Modern Statistics Laboratory -- Chapter. 10. A New Estimator for the GPD Parameters under the POT Approach -- Chapter. 11. A simple yet Robust Estimation of binned data: Egypt Income distribution and Geographical Inequality -- Chapter. 12. Supervised Machine Learning Classification Algorithms: Some Applications and Code Snippets for Practical Implementations in Python Programming -- Chapter. 13. Exploring the spatial variability and different determinants of co-existence of under-nutritional status among children in India through a Bayesian geo-additive multinomial regression model -- Chapter. 14. Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model -- Chapter. 15. Salvage Value from Deterioration (SVD): An Optimal Inventory Model for Chicken Egg Marketing -- Chapter. 16. Structural Equation Modeling with Stata: Illustration using a Population-Based, Nationally-Representative Dataset -- Chapter. 17. Time series forecasting of seasonal non-stationary climate data: A comparative study -- Chapter. 18. Weighted Hard and Soft Voting Ensemble Machine Learning CLASIFIERS: Application to Anaemia Diagnosis -- Chapter 19. Machine Learning Approaches for Handling Imbalances in Health Data Classification -- Chapter. 20. The Intersection of Data and Statistics with Sustainable Development Goals -- Chapter. 21. Teaching Data Science in Africa via Online Team-Based Learning.
Record Nr. UNINA-9910799496203321
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Synthetic Aperture Radar (SAR) Data Applications [[electronic resource] /] / edited by Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos
Synthetic Aperture Radar (SAR) Data Applications [[electronic resource] /] / edited by Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (282 pages)
Disciplina 621.3848
Collana Springer Optimization and Its Applications
Soggetto topico Mathematical optimization
Calculus of variations
Artificial intelligence
Statistics
Machine learning
Quantitative research
Calculus of Variations and Optimization
Artificial Intelligence
Machine Learning
Data Analysis and Big Data
Optimització matemàtica
Càlcul de variacions
Intel·ligència artificial
Aprenentatge automàtic
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 3-031-21225-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto End-to-End ATR Leveraging Deep Learning (M. Kreucher) -- Change Detection in SAR Images using Deep Learning Methods (Bovolo) -- Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval (M. Rysz) -- Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks (Semenov) -- A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery (L. Chen) -- Machine Learning Methods for SAR Interference Mitigation (Huang) -- Classification of SAR Images using Compact Convolutional Neural Networks (Ahishali) -- Multi-frequency Polarimetric SAR Data Analysis for Crop Type Classification using Random Forest (Mandal) -- Automatic Determination of Different Soil Types via Several Machine Learning Algorithms Employing Radarsat-2 SAR Image Polarization Coefficients (E. Acar) -- Ocean and coastal area information retrieval using SAR polarimetry (A. Buono).
Record Nr. UNISA-996508571403316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Synthetic Aperture Radar (SAR) Data Applications / / edited by Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos
Synthetic Aperture Radar (SAR) Data Applications / / edited by Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (282 pages)
Disciplina 621.3848
621.38485
Collana Springer Optimization and Its Applications
Soggetto topico Mathematical optimization
Calculus of variations
Artificial intelligence
Statistics
Machine learning
Quantitative research
Calculus of Variations and Optimization
Artificial Intelligence
Machine Learning
Data Analysis and Big Data
Optimització matemàtica
Càlcul de variacions
Intel·ligència artificial
Aprenentatge automàtic
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 3-031-21225-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto End-to-End ATR Leveraging Deep Learning (M. Kreucher) -- Change Detection in SAR Images using Deep Learning Methods (Bovolo) -- Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval (M. Rysz) -- Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks (Semenov) -- A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery (L. Chen) -- Machine Learning Methods for SAR Interference Mitigation (Huang) -- Classification of SAR Images using Compact Convolutional Neural Networks (Ahishali) -- Multi-frequency Polarimetric SAR Data Analysis for Crop Type Classification using Random Forest (Mandal) -- Automatic Determination of Different Soil Types via Several Machine Learning Algorithms Employing Radarsat-2 SAR Image Polarization Coefficients (E. Acar) -- Ocean and coastal area information retrieval using SAR polarimetry (A. Buono).
Record Nr. UNINA-9910645892803321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui