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Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment [[electronic resource] ] : With Examples in R and Python / / edited by Alina A. von Davier, Robert J. Mislevy, Jiangang Hao
| Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment [[electronic resource] ] : With Examples in R and Python / / edited by Alina A. von Davier, Robert J. Mislevy, Jiangang Hao |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (265 pages) |
| Disciplina | 371.26 |
| Collana | Methodology of Educational Measurement and Assessment |
| Soggetto topico |
Education
Psychometrics Social sciences - Statistical methods Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Psicometria Avaluació educativa Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-74394-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction. Computational Psychometrics: Towards a Principled Integration of Data Science and Machine Learning Techniques into Psychometrics (Alina A. von Davier, Robert Mislevy and Jiangang Hao) -- Part I. Conceptualization. 2. Next generation learning and assessment: what, why and how (Robert Mislevy) -- 3. Computational psychometrics (Alina A. von Davier, Kristen DiCerbo and Josine Verhagen) -- 4. Virtual performance-based assessments (Jessica Andrews-Todd, Robert Mislevy, Michelle LaMar and Sebastiaan de Klerk) -- 5. Knowledge Inference Models Used in Adaptive Learning (Maria Ofelia Z. San Pedro and Ryan S. Baker) -- Part II. Methodology. 6. Concepts and models from Psychometrics (Robert Mislevy and Maria Bolsinova) -- 7. Bayesian Inference in Large-Scale Computational Psychometrics (Gunter Maris, Timo Bechger and Maarten Marsman) -- 8. Data science perspectives (Jiangang Hao and Robert Mislevy) -- 9. Supervised machine learning (Jiangang Hao) -- 10. Unsupervised machine learning (Pak Chunk Wong) -- 11. AI and deep learning for educational research (Yuchi Huang and Saad M. Khan) -- 12. Time series and stochastic processes (Peter Halpin, Lu Ou and Michelle LaMar) -- 13. Social network analysis (Mengxiao Zhu) -- 14. Text mining and automated scoring (Michael Flor and Jiangang Hao). |
| Record Nr. | UNINA-9910520094903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Engineering and Management : Second International Conference, ICDEM 2010, Tiruchirappalli, India, July 29-31, 2010. Revised Selected Papers / / edited by Rajkumar Kannan, Frederic Andres
| Data Engineering and Management : Second International Conference, ICDEM 2010, Tiruchirappalli, India, July 29-31, 2010. Revised Selected Papers / / edited by Rajkumar Kannan, Frederic Andres |
| Edizione | [1st ed. 2012.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 |
| Descrizione fisica | 1 online resource (356 p. 139 illus.) |
| Disciplina | 005.1 |
| Collana | Programming and Software Engineering |
| Soggetto topico |
Mineria de dades
Gestió de bases de dades Biblioteques digitals Software engineering Computer logic Programming languages (Electronic computers) Artificial intelligence Mathematical logic Computer communication systems Software Engineering Logics and Meanings of Programs Programming Languages, Compilers, Interpreters Artificial Intelligence Mathematical Logic and Formal Languages Computer Communication Networks |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-642-27872-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465934503316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Data mining with SPSS modeler : theory, exercises and solutions / / Tilo Wendler, Sören Gröttrup
| Data mining with SPSS modeler : theory, exercises and solutions / / Tilo Wendler, Sören Gröttrup |
| Autore | Wendler Tilo |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1285 pages) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-54338-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466401303316 |
Wendler Tilo
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Data mining with SPSS modeler : theory, exercises and solutions / / Tilo Wendler, Sören Gröttrup
| Data mining with SPSS modeler : theory, exercises and solutions / / Tilo Wendler, Sören Gröttrup |
| Autore | Wendler Tilo |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1285 pages) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-54338-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484742203321 |
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Wendler Tilo
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data science and security : proceedings of IDSCS 2021 / / editors, Samiksha Shukla [et al.]
| Data science and security : proceedings of IDSCS 2021 / / editors, Samiksha Shukla [et al.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xiv, 489 pages) |
| Disciplina | 006.3 |
| Collana | Lecture notes in networks and systems |
| Soggetto topico |
Artificial intelligence
Computer security Data mining Mineria de dades Seguretat informàtica Intel·ligència artificial |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-16-4486-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- Towards a Knowledge Centric Semantic Approach for Text Summarization -- 1 Introduction -- 2 Related Works -- 3 Proposed Architecture -- 4 Implementation -- 5 Performance Evaluation and Results -- 6 Conclusion -- References -- Detection of Abnormal Red Blood Cells Using Features Dependent on Morphology and Rotation -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Method -- 3.1 Determine the ROI -- 3.2 Features Determination -- 4 Quality Assessment -- 5 Experimental Results -- 6 Conclusion -- 7 Future Scope -- References -- A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning -- 1 Introduction -- 2 Background -- 2.1 What is Privacy in Machine Learning -- 3 Classification of Machine Learning Attacks -- 3.1 Explicit Attack -- 3.2 Implicit Attack -- 4 Privacy-Preserving Mechanisms -- 4.1 Data Aggregation -- 4.2 Training Phase -- 4.3 Inference Phase -- 5 Privacy-Enhancing Execution Models and Environments -- 5.1 Federated Learning -- 5.2 Split Learning -- 5.3 Trusted Execution Environment -- 6 Comparative Analysis -- 7 Conclusion -- References -- Deep Learning Methods for Intrusion Detection System -- 1 Introduction -- 2 Related Work -- 3 Proposed Deep Learning Based Intrusion Detection System -- 4 System Implementation -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 Finding Optimal Parameters in DNN -- 4.4 Finding Optimal Parameters in CNN -- 4.5 Classification -- 5 Results -- 6 Conclusion -- References -- Adaptive Neuro Fuzzy Approach for Assessment of Learner's Domain Knowledge -- 1 Introduction -- 2 Literature Review -- 3 Dataset Preparation and ANFIS Model Development -- 4 ANFIS Model: Testing and Validation -- 5 Conclusion -- References -- Comparison of Full Training and Transfer Learning in Deep Learning for Image Classification -- 1 Introduction.
2 Literature Review -- 3 Method -- 3.1 Transfer Learning Approach -- 3.2 Full Training Approach -- 4 Results and Analysis -- 5 Conclusion -- References -- Physical Unclonable Function and OAuth 2.0 Based Secure Authentication Scheme for Internet of Medical Things -- 1 Introduction -- 2 IoMT and Security -- 2.1 IoMT Architecture -- 2.2 Security Attacks -- 2.3 Authentication -- 2.4 PUF -- 2.5 OAuth 2.0 -- 3 Literature Review -- 4 Proposed Model -- 4.1 Proposed Architecture -- 4.2 Algorithm -- 4.3 Enrolment Phase -- 4.4 Authentication Phase -- 4.5 Data Transmission Phase -- 5 Analysis of the Proposed Scheme -- 5.1 Replay Attacks -- 5.2 Impersonation Attacks -- 5.3 Eavesdropping Attacks -- 5.4 Stolen Device -- 6 Conclusion -- References -- Sensitivity Analysis of a Multilayer Perceptron Network for Cervical Cancer Risk Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Sensitivity Analysis -- 3.3 Algorithm -- 4 Results and Discussions -- 4.1 Number of Inputs and Accuracy -- 4.2 Number of Epochs and Accuracy -- 4.3 NNIHL and Accuracy -- 4.4 Performance Comparison -- 5 Conclusions -- References -- Data Encryption and Decryption Techniques Using Line Graphs -- 1 Introduction -- 2 Related Work -- 3 Proposed Algorithm -- 3.1 Encryption Algorithm -- 3.2 Decryption Algorithm -- 4 Encryption and Decryption of the Plaintext - `Crypto' -- 4.1 Encryption -- 4.2 Decryption -- 5 Conclusion -- References -- Aerial Image Enhanced by Using Dark Channel Prior and Retinex Algorithms Based on HSV Color Space -- 1 Introduction -- 2 Proposed Method -- 2.1 DCP Algorithm -- 2.2 HSV Color Space -- 2.3 MSR Algorithm -- 2.4 The Combination of Enhancement Compounds -- 3 Determine the Quality Assessment -- 4 Result and Discussion -- 5 Conclusions -- References -- CARPM: Comparative Analysis of Routing Protocols in MANET -- 1 Introduction. 2 Related Work -- 3 Comparative Analysis of Proposed Routing protocol with DSR and AODV and PA-DSR and GC-DSR -- 3.1 Proactive Routing Protocols -- 3.2 Reactive Routing Protocols -- 3.3 Hybrid Routing Protocols -- 4 Challenges or Drawbacks in DSR, AODV and ZRP -- 5 Proposed Green Corridor Protocol -- 5.1 Proposed Model Algorithm Pseudo-code -- 5.2 Results and Discussion -- 6 Conclusion -- References -- Content-Restricted Boltzmann Machines for Diet Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results and Discussions -- 5 Conclusion -- References -- PIREN: Prediction of Intermediary Readers' Emotion from News-Articles -- 1 Introduction -- 2 Literature Survey and Related Works -- 3 Proposed Methodology -- 4 Implementation -- 5 Results and Performance Evaluation -- 6 Conclusion -- References -- Automated Organic Web Harvesting on Web Data for Analytics -- 1 Introduction -- 2 Literature Review -- 3 Overview of Web Scraping System -- 4 The Proposed System -- 5 Experiments and Results -- 6 Conclusion and Future Work -- References -- Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition -- 1 Introduction -- 2 Review of Literature -- 3 Methodology -- 3.1 Feature Extraction -- 3.2 Classification -- 4 Experimental Results -- 5 Conclusion -- References -- ODFWR: An Ontology Driven Framework for Web Service Recommendation -- 1 Introduction -- 2 Related Work -- 3 Proposed System Architecture -- 4 Implementation and Performance Evaluation -- 5 Conclusions -- References -- Smart Contract Security and Privacy Taxonomy, Tools, and Challenges -- 1 Introduction -- 2 Literature Review -- 3 Taxonomy for Smart Contract Vulnerabilities -- 4 Proposed Taxonomy for Blockchain Smart Contracts -- 4.1 OWASP Risk Rating Methodology -- 4.2 Proposed Taxonomy. 5 Tools and Methods Used for the Testing -- 6 Open Challenges -- 6.1 Universal Taxonomy -- 6.2 AI-Based Security Tools -- 6.3 The Mechanism to Recall Smart Contract -- 6.4 Auditing Tool that can Support More than One Language -- 6.5 Strategy for Testing a Smart Contract -- 7 Future Work and Conclusion -- References -- Heteroskedasticity Analysis During Operational Data Processing of Radio Electronic Systems -- 1 Introduction -- 2 Literature Analysis and Problem Statement -- 3 Models of Diagnostic Variable in the Case of Heteroskedasticity -- 4 Method for Taking into Account Heteroskedasticity During Analysis of the Diagnostic Variable Trend -- 5 Conclusion -- References -- Role of Data Science in the Field of Genomics and Basic Analysis of Raw Genomic Data Using Python -- 1 Introduction -- 2 Literature Review -- 3 Methodology for Analysing Genomic Data Using Python -- 3.1 Experimental Setup -- 4 Results and Discussion -- 5 Recent Findings in the Field of Genomics -- 6 Conclusions -- References -- Automatic Detection of Smoke in Videos Relying on Features Analysis Using RGB and HSV Colour Spaces -- 1 Introduction -- 2 Literature Review -- 3 Suggested Method -- 3.1 Cut the Video into Frames -- 3.2 Important Video Frame Determination -- 3.3 Smoke Detection Depending on the Features -- 4 Accuracy Meters -- 5 Result and Dissection -- 6 Conclusions -- References -- A Comparative Study of the Performance of Gait Recognition Using Gait Energy Image and Shannon's Entropy Image with CNN -- 1 Introduction -- 2 Literature Survey -- 3 Gait and Gait Phases -- 4 Experiments and Results -- 5 Conclusion -- References -- OntoJudy: A Ontology Approach for Content-Based Judicial Recommendation Using Particle Swarm Optimisation and Structural Topic Modelling -- 1 Introduction -- 2 Related Work -- 3 Proposed System Architecture -- 4 Implementation. 5 Results and Performance Evaluation -- 6 Conclusions -- References -- Classifying Emails into Spam or Ham Using ML Algorithms -- 1 Introduction -- 2 Related Works -- 2.1 Impact of Feature Selection Technique on Email Classification -- 2.2 A Hybrid Algorithm for Malicious Spam Detection in Email through Machine Learning -- 2.3 Study on the Effect of Preprocessing Methods for Spam Email Detection -- 2.4 Review Web Spam Detection Using Data Mining -- 2.5 Machine Learning-Based Spam Email Detection -- 3 Methodology -- 3.1 Naive Bayes -- 3.2 Support Vector Machine (SVMs) -- 3.3 Random Forest -- 3.4 Decision Tree -- 4 Experiment and Result Analysis -- 4.1 Dataset -- 5 Conclusion -- References -- Rice Yield Forecasting in West Bengal Using Hybrid Model -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Collection -- 3.2 ARIMA Model -- 3.3 ANN Model -- 3.4 Hybrid Model -- 3.5 Performance Metrics -- 4 Experiments -- 5 Conclusion and Future Work -- References -- An Inventory Model for Growing Items with Deterioration and Trade Credit -- 1 Introduction -- 2 Modal Formation, Notations and Assumptions -- 3 Analysis -- 4 Particular Case -- 5 Solution Procedure -- 6 Sensitivity Analysis -- 7 Conclusion -- References -- A Deep Learning Based Approach for Classification of News as Real or Fake -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 4 Experiment Analysis -- 4.1 Data Pre-processing -- 4.2 Model Analysis -- 4.3 Experimental Result -- 5 Conclusion and Future Aspects -- References -- User Authentication with Graphical Passwords using Hybrid Images and Hash Function -- 1 Introduction -- 2 Literature Review -- 3 Proposed System -- 3.1 Algorithm -- 4 Working Example -- 4.1 Registration Process -- 4.2 Login Process -- 5 Security Analysis -- 6 Conclusion and Future Work -- References. UAS Cyber Security Hazards Analysis and Approach to Qualitative Assessment. |
| Record Nr. | UNINA-9910495223403321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data science techniques for cryptocurrency blockchains / / Innar Liiv
| Data science techniques for cryptocurrency blockchains / / Innar Liiv |
| Autore | Liiv Innar |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (117 pages) |
| Disciplina | 005.74 |
| Collana | Behaviormetrics: Quantitative Approaches to Human Behavior |
| Soggetto topico |
Statistics
Cadena de blocs (Bases de dades) Criptomoneda Mineria de dades Data mining Big data |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-2418-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Preface -- Disclaimer Regarding Terminology -- Acknowledgements -- Contents -- 1 Understanding the Data Model -- 1.1 Data Model -- 1.2 Data Examples -- 1.3 Exercises and Questions for Discussion -- References -- 2 Exploration with Structured Query Language -- 2.1 Example Queries -- 2.2 Exercises and Questions for Discussion -- References -- 3 Association Rules -- 3.1 Basic Concepts -- 3.2 Association Rules in Cryptocurrency Blockchains -- 3.3 Exercises and Questions for Discussion -- References -- 4 Clustering -- 4.1 Basic Concepts -- 4.2 Similarity Measurement Example -- 4.3 K-Means Clustering and Hierarchical Clustering -- 4.4 Clustering Larger Cryptocurrency Balance Datasets -- 4.5 Exercises and Questions for Discussion -- References -- 5 Classification -- 5.1 Basic Concepts -- 5.2 Classifying Larger Cryptocurrency Balance Datasets -- 5.3 Exercises and Questions for Discussion -- References -- 6 Visualization -- 6.1 Basic Concepts -- 6.2 Color-Coding and Reordering a Data Table -- 6.3 Multidimensional Scaling -- 6.4 Parallel Coordinates -- 6.5 Pixel-Oriented Visualization -- 6.6 Exercises and Questions for Discussion -- References -- 7 Network Science -- 7.1 Basic Concepts -- 7.2 Network Data -- 7.3 Structural Properties of Networks -- 7.4 Community Detection in Networks -- 7.5 Visualization of Networks -- 7.6 Constructing Network Data for Cryptocurrency Blockchains -- 7.7 Visualizing Larger Cryptocurrency Networks -- 7.8 Exercises and Questions for Discussion -- References -- 8 Conclusions -- References -- Index. |
| Record Nr. | UNISA-996466415203316 |
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Liiv Innar
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| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Data science techniques for cryptocurrency blockchains / / Innar Liiv
| Data science techniques for cryptocurrency blockchains / / Innar Liiv |
| Autore | Liiv Innar |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (117 pages) |
| Disciplina | 005.74 |
| Collana | Behaviormetrics: Quantitative Approaches to Human Behavior |
| Soggetto topico |
Statistics
Cadena de blocs (Bases de dades) Criptomoneda Mineria de dades Data mining Big data |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-2418-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Preface -- Disclaimer Regarding Terminology -- Acknowledgements -- Contents -- 1 Understanding the Data Model -- 1.1 Data Model -- 1.2 Data Examples -- 1.3 Exercises and Questions for Discussion -- References -- 2 Exploration with Structured Query Language -- 2.1 Example Queries -- 2.2 Exercises and Questions for Discussion -- References -- 3 Association Rules -- 3.1 Basic Concepts -- 3.2 Association Rules in Cryptocurrency Blockchains -- 3.3 Exercises and Questions for Discussion -- References -- 4 Clustering -- 4.1 Basic Concepts -- 4.2 Similarity Measurement Example -- 4.3 K-Means Clustering and Hierarchical Clustering -- 4.4 Clustering Larger Cryptocurrency Balance Datasets -- 4.5 Exercises and Questions for Discussion -- References -- 5 Classification -- 5.1 Basic Concepts -- 5.2 Classifying Larger Cryptocurrency Balance Datasets -- 5.3 Exercises and Questions for Discussion -- References -- 6 Visualization -- 6.1 Basic Concepts -- 6.2 Color-Coding and Reordering a Data Table -- 6.3 Multidimensional Scaling -- 6.4 Parallel Coordinates -- 6.5 Pixel-Oriented Visualization -- 6.6 Exercises and Questions for Discussion -- References -- 7 Network Science -- 7.1 Basic Concepts -- 7.2 Network Data -- 7.3 Structural Properties of Networks -- 7.4 Community Detection in Networks -- 7.5 Visualization of Networks -- 7.6 Constructing Network Data for Cryptocurrency Blockchains -- 7.7 Visualizing Larger Cryptocurrency Networks -- 7.8 Exercises and Questions for Discussion -- References -- 8 Conclusions -- References -- Index. |
| Record Nr. | UNINA-9910488720903321 |
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Liiv Innar
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| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Dimensionality reduction in data science / / Max Garzon [and five others]
| Dimensionality reduction in data science / / Max Garzon [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (268 pages) : illustrations |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Dades massives Mineria de dades Data mining - Computer programs |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-05371-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Acronyms -- 1 What Is Data Science (DS)? -- 1.1 Major Families of Data Science Problems -- 1.1.1 Classification Problems -- 1.1.2 Prediction Problems -- 1.1.3 Clustering Problems -- 1.2 Data, Big Data, and Pre-processing -- 1.2.1 What Is Data? -- 1.2.2 Big Data -- 1.2.3 Data Cleansing -- 1.2.3.1 Duplication -- 1.2.3.2 Fixing/Removing Errors -- 1.2.3.3 Missing Data -- 1.2.3.4 Outliers -- 1.2.3.5 Multicollinearity -- 1.2.4 Data Visualization -- 1.2.5 Data Understanding -- 1.3 Populations and Data Sampling -- 1.3.1 Sampling -- 1.3.2 Training, Testing, and Validation -- 1.4 Overview and Scope -- 1.4.1 Prerequisites and Layout -- 1.4.2 Data Science Methodology -- 1.4.3 Scope of the Book -- Reference -- 2 Solutions to Data Science Problems -- 2.1 Conventional Statistical Solutions -- 2.1.1 Linear Multiple Regression Model: Continuous Response -- 2.1.1.1 Akaike Information Criterion (AIC) -- 2.1.1.2 Bayesian Information Criterion (BIC) -- 2.1.1.3 Adjusted R-Squared -- 2.1.2 Logistic Regression: Categorical Response -- 2.1.3 Variable Selection and Model Building -- 2.1.4 Generalized Linear Model (GLM) -- 2.1.5 Decision Trees -- 2.1.6 Bayesian Learning -- 2.2 Machine Learning Solutions: Supervised -- 2.2.1 k-Nearest Neighbors (kNN) -- 2.2.2 Ensemble Methods -- 2.2.3 Support Vector Machines (SVMs) -- 2.2.4 Neural Networks (NNs) -- 2.3 Machine Learning Solutions: Unsupervised -- 2.3.1 Hard Clustering -- 2.3.2 Soft Clustering -- 2.4 Controls, Evaluation, and Assessment -- 2.4.1 Evaluation Methods -- 2.4.2 Metrics for Assessment -- References -- 3 What Is Dimensionality Reduction (DR)? -- 3.1 Dimensionality Reduction -- 3.2 Major Approaches to Dimensionality Reduction -- 3.2.1 Conventional Statistical Approaches -- 3.2.2 Geometric Approaches -- 3.2.3 Information-Theoretic Approaches -- 3.2.4 Molecular Computing Approaches.
3.3 The Blessings of Dimensionality -- References -- 4 Conventional Statistical Approaches -- 4.1 Principal Component Analysis (PCA) -- 4.1.1 Obtaining the Principal Components -- 4.1.2 Singular Value Decomposition (SVD) -- 4.2 Nonlinear PCA -- 4.2.1 Kernel PCA -- 4.2.2 Independent Component Analysis (ICA) -- 4.3 Nonnegative Matrix Factorization (NMF) -- 4.3.1 Approximate Solutions -- 4.3.2 Clustering and Other Applications -- 4.4 Discriminant Analysis -- 4.4.1 Linear Discriminant Analysis (LDA) -- 4.4.2 Quadratic Discriminant Analysis (QDA) -- 4.5 Sliced Inverse Regression (SIR) -- References -- 5 Geometric Approaches -- 5.1 Introduction to Manifolds -- 5.2 Manifold Learning Methods -- 5.2.1 Multi-Dimensional Scaling (MDS) -- 5.2.1.1 Classical MDS: Spectral Approach -- 5.2.1.2 Metric MDS: Optimization-Based Approach -- 5.2.2 Isometric Mapping (ISOMAP) -- 5.2.3 t-Stochastic Neighbor Embedding ( t-SNE ) -- 5.3 Exploiting Randomness (RND) -- References -- 6 Information-Theoretic Approaches -- 6.1 Shannon Entropy (H) -- 6.2 Reduction by Conditional Entropy -- 6.3 Reduction by Iterated Conditional Entropy -- 6.4 Reduction by Conditional Entropy on Targets -- 6.5 Other Variations -- References -- 7 Molecular Computing Approaches -- 7.1 Encoding Abiotic Data into DNA -- 7.2 Deep Structure of DNA Spaces -- 7.2.1 Structural Properties of DNA Spaces -- 7.2.2 Noncrosshybridizing (nxh) Bases -- 7.3 Reduction by Genomic Signatures -- 7.3.1 Background -- 7.3.2 Genomic Signatures -- 7.4 Reduction by Pmeric Signatures -- References -- 8 Statistical Learning Approaches -- 8.1 Reduction by Multiple Regression -- 8.2 Reduction by Ridge Regression -- 8.3 Reduction by Lasso Regression -- 8.4 Selection Versus Shrinkage -- 8.5 Further Refinements -- References -- 9 Machine Learning Approaches -- 9.1 Autoassociative Feature Encoders -- 9.1.1 Undercomplete Autoencoders. 9.1.2 Sparse Autoencoders -- 9.1.3 Variational Autoencoders -- 9.1.4 Dimensionality Reduction in MNIST Images -- 9.2 Neural Feature Selection -- 9.2.1 Facial Features, Expressions, and Displays -- 9.2.2 The Cohn-Kanade Dataset -- 9.2.3 Primary and Derived Features -- 9.3 Other Methods -- References -- 10 Metaheuristics of DR Methods -- 10.1 Exploiting Feature Grouping -- 10.2 Exploiting Domain Knowledge -- 10.2.1 What Is Domain Knowledge? -- 10.2.2 Domain Knowledge for Dimensionality Reduction -- 10.3 Heuristic Rules for Feature Selection, Extraction, and Number -- 10.4 About Explainability of Solutions -- 10.4.1 What Is Explainability? -- 10.4.1.1 Outcome Explanations -- 10.4.1.2 Model Explanations -- 10.4.2 Explainability in Dimensionality Reduction -- 10.5 Choosing Wisely -- 10.6 About the Curse of Dimensionality -- 10.7 About the No-Free-Lunch Theorem (NFL) -- References -- 11 Appendices -- 11.1 Statistics and Probability Background -- 11.1.1 Commonly Used Discrete Distributions -- 11.1.2 Commonly Used Continuous Distributions -- 11.1.3 Major Results in Probability and Statistics -- 11.2 Linear Algebra Background -- 11.2.1 Fields, Vector Spaces and Subspaces -- 11.2.2 Linear Independence, Bases and Dimension -- 11.2.3 Linear Transformations and Matrices -- 11.2.4 Eigenvalues and Spectral Decomposition -- 11.3 Computer Science Background -- 11.3.1 Computational Science and Complexity -- 11.3.2 Machine Learning -- 11.4 Typical Data Science Problems -- 11.5 A Sample of Common and Big Datasets -- 11.6 Computing Platforms -- 11.6.1 The Environment R -- 11.6.2 Python Environments -- References. |
| Record Nr. | UNISA-996483154003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Dimensionality reduction in data science / / Max Garzon [and five others]
| Dimensionality reduction in data science / / Max Garzon [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (268 pages) : illustrations |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Dades massives Mineria de dades Data mining - Computer programs |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-05371-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Acronyms -- 1 What Is Data Science (DS)? -- 1.1 Major Families of Data Science Problems -- 1.1.1 Classification Problems -- 1.1.2 Prediction Problems -- 1.1.3 Clustering Problems -- 1.2 Data, Big Data, and Pre-processing -- 1.2.1 What Is Data? -- 1.2.2 Big Data -- 1.2.3 Data Cleansing -- 1.2.3.1 Duplication -- 1.2.3.2 Fixing/Removing Errors -- 1.2.3.3 Missing Data -- 1.2.3.4 Outliers -- 1.2.3.5 Multicollinearity -- 1.2.4 Data Visualization -- 1.2.5 Data Understanding -- 1.3 Populations and Data Sampling -- 1.3.1 Sampling -- 1.3.2 Training, Testing, and Validation -- 1.4 Overview and Scope -- 1.4.1 Prerequisites and Layout -- 1.4.2 Data Science Methodology -- 1.4.3 Scope of the Book -- Reference -- 2 Solutions to Data Science Problems -- 2.1 Conventional Statistical Solutions -- 2.1.1 Linear Multiple Regression Model: Continuous Response -- 2.1.1.1 Akaike Information Criterion (AIC) -- 2.1.1.2 Bayesian Information Criterion (BIC) -- 2.1.1.3 Adjusted R-Squared -- 2.1.2 Logistic Regression: Categorical Response -- 2.1.3 Variable Selection and Model Building -- 2.1.4 Generalized Linear Model (GLM) -- 2.1.5 Decision Trees -- 2.1.6 Bayesian Learning -- 2.2 Machine Learning Solutions: Supervised -- 2.2.1 k-Nearest Neighbors (kNN) -- 2.2.2 Ensemble Methods -- 2.2.3 Support Vector Machines (SVMs) -- 2.2.4 Neural Networks (NNs) -- 2.3 Machine Learning Solutions: Unsupervised -- 2.3.1 Hard Clustering -- 2.3.2 Soft Clustering -- 2.4 Controls, Evaluation, and Assessment -- 2.4.1 Evaluation Methods -- 2.4.2 Metrics for Assessment -- References -- 3 What Is Dimensionality Reduction (DR)? -- 3.1 Dimensionality Reduction -- 3.2 Major Approaches to Dimensionality Reduction -- 3.2.1 Conventional Statistical Approaches -- 3.2.2 Geometric Approaches -- 3.2.3 Information-Theoretic Approaches -- 3.2.4 Molecular Computing Approaches.
3.3 The Blessings of Dimensionality -- References -- 4 Conventional Statistical Approaches -- 4.1 Principal Component Analysis (PCA) -- 4.1.1 Obtaining the Principal Components -- 4.1.2 Singular Value Decomposition (SVD) -- 4.2 Nonlinear PCA -- 4.2.1 Kernel PCA -- 4.2.2 Independent Component Analysis (ICA) -- 4.3 Nonnegative Matrix Factorization (NMF) -- 4.3.1 Approximate Solutions -- 4.3.2 Clustering and Other Applications -- 4.4 Discriminant Analysis -- 4.4.1 Linear Discriminant Analysis (LDA) -- 4.4.2 Quadratic Discriminant Analysis (QDA) -- 4.5 Sliced Inverse Regression (SIR) -- References -- 5 Geometric Approaches -- 5.1 Introduction to Manifolds -- 5.2 Manifold Learning Methods -- 5.2.1 Multi-Dimensional Scaling (MDS) -- 5.2.1.1 Classical MDS: Spectral Approach -- 5.2.1.2 Metric MDS: Optimization-Based Approach -- 5.2.2 Isometric Mapping (ISOMAP) -- 5.2.3 t-Stochastic Neighbor Embedding ( t-SNE ) -- 5.3 Exploiting Randomness (RND) -- References -- 6 Information-Theoretic Approaches -- 6.1 Shannon Entropy (H) -- 6.2 Reduction by Conditional Entropy -- 6.3 Reduction by Iterated Conditional Entropy -- 6.4 Reduction by Conditional Entropy on Targets -- 6.5 Other Variations -- References -- 7 Molecular Computing Approaches -- 7.1 Encoding Abiotic Data into DNA -- 7.2 Deep Structure of DNA Spaces -- 7.2.1 Structural Properties of DNA Spaces -- 7.2.2 Noncrosshybridizing (nxh) Bases -- 7.3 Reduction by Genomic Signatures -- 7.3.1 Background -- 7.3.2 Genomic Signatures -- 7.4 Reduction by Pmeric Signatures -- References -- 8 Statistical Learning Approaches -- 8.1 Reduction by Multiple Regression -- 8.2 Reduction by Ridge Regression -- 8.3 Reduction by Lasso Regression -- 8.4 Selection Versus Shrinkage -- 8.5 Further Refinements -- References -- 9 Machine Learning Approaches -- 9.1 Autoassociative Feature Encoders -- 9.1.1 Undercomplete Autoencoders. 9.1.2 Sparse Autoencoders -- 9.1.3 Variational Autoencoders -- 9.1.4 Dimensionality Reduction in MNIST Images -- 9.2 Neural Feature Selection -- 9.2.1 Facial Features, Expressions, and Displays -- 9.2.2 The Cohn-Kanade Dataset -- 9.2.3 Primary and Derived Features -- 9.3 Other Methods -- References -- 10 Metaheuristics of DR Methods -- 10.1 Exploiting Feature Grouping -- 10.2 Exploiting Domain Knowledge -- 10.2.1 What Is Domain Knowledge? -- 10.2.2 Domain Knowledge for Dimensionality Reduction -- 10.3 Heuristic Rules for Feature Selection, Extraction, and Number -- 10.4 About Explainability of Solutions -- 10.4.1 What Is Explainability? -- 10.4.1.1 Outcome Explanations -- 10.4.1.2 Model Explanations -- 10.4.2 Explainability in Dimensionality Reduction -- 10.5 Choosing Wisely -- 10.6 About the Curse of Dimensionality -- 10.7 About the No-Free-Lunch Theorem (NFL) -- References -- 11 Appendices -- 11.1 Statistics and Probability Background -- 11.1.1 Commonly Used Discrete Distributions -- 11.1.2 Commonly Used Continuous Distributions -- 11.1.3 Major Results in Probability and Statistics -- 11.2 Linear Algebra Background -- 11.2.1 Fields, Vector Spaces and Subspaces -- 11.2.2 Linear Independence, Bases and Dimension -- 11.2.3 Linear Transformations and Matrices -- 11.2.4 Eigenvalues and Spectral Decomposition -- 11.3 Computer Science Background -- 11.3.1 Computational Science and Complexity -- 11.3.2 Machine Learning -- 11.4 Typical Data Science Problems -- 11.5 A Sample of Common and Big Datasets -- 11.6 Computing Platforms -- 11.6.1 The Environment R -- 11.6.2 Python Environments -- References. |
| Record Nr. | UNINA-9910734855203321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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. | UNISA-996466565603316 |
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Zhang Xiaomei
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Soggetto topico
-
Mineria de dades
(42)
- Data mining (24)
- Big data (12)
- Dades massives (8)
- Artificial intelligence (7)