1.
ＥＢ
by Guanghui Lan
目次情報:
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Machine Learning Models
Convex Optimization Theory
Deterministic Convex Optimization
Stochastic Convex Optimization
Convex Finite-sum and Distributed Optimization
Nonconvex Optimization
Projection-free Methods
Operator Sliding and Decentralized Optimization
Machine Learning Models
Convex Optimization Theory
Deterministic Convex Optimization
2.
ＥＢ
edited by Mohd Azraai Mohd Razman, Jessnor Arif Mat Jizat, Nafrizuan Mat Yahya, Hyun Myung, Amar Faiz Zainal Abidin, Mohamad Shaiful Abdul Karim
目次情報:
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An Adaptive Self-Assessment Model for Improving Student Performance in Language Learning using Massive Open Online Course (MOOC)
Assessment on Average Participation Versus Bialek’s Methods for Transmission Usage Evaluation Scheme
Field-effect Transistor-based Biosensor Optimization: Single Versus Array Silicon Nanowires Configuration
Top-Down Fabrication of Silicon Nanogap for Detection of Dengue Virus (DENV)
Design and Performance Analysis of IoT Based Sensor System using LoRa
A PSPT-MAC mechanism for congestion avoidance in Wireless Body Area Network
A Planar Slotted RCS based UWB RFID Tag on A PCB and A Flexible Substrate for Packaging Application
Design and Analysis of Automated Inspection System for Relays Fault Detection
Visible Light Communication-Based Indoor Notification System for Blind People
An Adaptive Self-Assessment Model for Improving Student Performance in Language Learning using Massive Open Online Course (MOOC)
Assessment on Average Participation Versus Bialek’s Methods for Transmission Usage Evaluation Scheme
Field-effect Transistor-based Biosensor Optimization: Single Versus Array Silicon Nanowires Configuration
3.
ＥＢ
edited by Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名:
Information Systems and Applications, incl. Internet/Web, and HCI ; 12112
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-3-030-59410-7
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Advanced database and web applications.-Big data
Data mining
Machine learning for database
Data warehouse and OLAP
Information retrieval
Data model and query language
Query processing
Optimization
Recommendation systems
Data quality and credibility
Multimedia databases
Temporal and spatial databases
Data streams and time-series data
Semantic web and knowledge management
Graph data management
Social network analytics
Bio and health informatics
Blockchain and parallel/distributed systems
Security
Privacy and Trust
Databases for emerging hardware
Advanced database and web applications.-Big data
Data mining
Machine learning for database
4.
ＥＢ
by Ovidiu Calin
目次情報:
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Introductory Problems
Activation Functions
Cost Functions
Finding Minima Algorithms
Abstract Neurons
Neural Networks
Approximation Theorems
Learning with One-dimensional Inputs
Universal Approximators
Exact Learning
Information Representation
Information Capacity Assessment
Output Manifolds
Neuromanifolds
Pooling
Convolutional Networks
Recurrent Neural Networks
Classification
Generative Models
Stochastic Networks
Hints and Solutions
Introductory Problems
Activation Functions
Cost Functions
5.
ＥＢ
edited by Hemanth Venkateswara, Sethuraman Panchanathan
目次情報:
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Preface
Part I: Introduction
Chapter 1: Introduction to Domain Adaptation
Chapter 2: Shallow Domain Adaptation
Part II: Domain Alignment in the Feature Space
Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation
Chapter 5: Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation
Part III: Domain Alignment in the Image Space
Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network
Chapter 7: Domain Adaptation via Image to Image Translation
Chapter 8: Domain Adaptation via Image Style Transfer
Part IV: Future Directions in Domain Adaptation
Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation
Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation
Chapter 11: Universal Domain Adaptation
Chapter 12: Multi-source Domain Adaptation by Deep CockTail Networks
Chapter 13: Zero-Shot Task Transfer
Preface
Part I: Introduction
Chapter 1: Introduction to Domain Adaptation
6.
ＥＢ
by Dietmar P.F. Möller
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名:
SpringerBriefs on Cyber Security Systems and Networks ;
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-3-030-60570-4
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1.Cybersecurity in Digital Transformation
2.Introduction to Cybersecurity
3.Threat Intelligence
4.Intrusion Detection and Prevention
5.Machine Learning and Deep Learning
6.Attach Models and Scenarios
7.Cybersecurity Ontology
8.Cybersecurity Leadership
1.Cybersecurity in Digital Transformation
2.Introduction to Cybersecurity
3.Threat Intelligence
7.
ＥＢ
edited by George A. Tsihrintzis, Lakhmi C. Jain
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名:
Learning and Analytics in Intelligent Systems ; 18
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オンライン:
https://doi.org/10.1007/978-3-030-49724-8
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Chapter 1: Introduction to Deep Learning-based Technological Applications
Chapter 2: Vision to Language: Methods, Metrics and Datasets
Chapter 3: Deep Learning Techniques for Geospatial Data Analysis
Chapter 4: Deep Learning Approaches in Food Recognition
Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding
Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks
Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area
Chapter 8: Deep Learning Methods in Electroencephalography
Chapter 1: Introduction to Deep Learning-based Technological Applications
Chapter 2: Vision to Language: Methods, Metrics and Datasets
Chapter 3: Deep Learning Techniques for Geospatial Data Analysis
8.
ＥＢ
by Farkhund Iqbal, Mourad Debbabi, Benjamin C. M. Fung
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名:
International Series on Computer Entertainment and Media Technology ;
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-3-030-61675-5
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1. Cybersecurity And Cybercrime Investigation
2. Machine Learning Framework For Messaging Forensics
3. Header-Level Investigation And Analyzing Network Information
4. Authorship Analysis Approaches
5. Authorship Analysis - Writeprint Mining For Authorship Attribution
6. Authorship Attribution With Few Training Samples
7. Authorship Characterization
8. Authorship Verification
9. Authorship Attribution Using Customized Associative Classification
10. Criminal Information Mining
11. Artificial Intelligence And Digital Forensics
1. Cybersecurity And Cybercrime Investigation
2. Machine Learning Framework For Messaging Forensics
3. Header-Level Investigation And Analyzing Network Information
9.
ＥＢ
edited by Hao Dong, Zihan Ding, Shanghang Zhang
目次情報:
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Preface
Contributors
Acknowledgements
Mathematical Notation
Acronyms
Introduction
Part 1: Foundamentals
Chapter 1: Introduction to Deep Learning
Chapter 2: Introduction to Reinforcement Learning
Chapter 3: Taxonomy of Reinforcement Learning Algorithms
Chapter 4: Deep Q-Networks
Chapter 5: Policy Gradient
Chapter 6: Combine Deep Q-Networks with Actor-Critic
Part II: Research
Chapter 7: Challenges of Reinforcement Learning
Chapter 8: Imitation Learning
Chapter 9: Integrating Learning and Planning
Chapter 10: Hierarchical Reinforcement Learning
Chapter 11: Multi-Agent Reinforcement Learning
Chapter 12: Parallel Computing
Part III: Applications
Chapter 13: Learning to Run
Chapter 14: Robust Image Enhancement
Chapter 15: AlphaZero
Chapter 16: Robot Learning in Simulation
Chapter 17: Arena Platform for Multi-Agent Reinforcement Learning
Chapter 18: Tricks of Implementation
Part IV: Summary
Chapter 19: Algorithm Table
Chapter 20: Algorithm Cheatsheet
Preface
Contributors
Acknowledgements
10.
ＥＢ
by Tony Pourmohamad, Herbert K. H. Lee
目次情報:
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1. Computer experiments
2. Surrogate models
3. Unconstrained optimization
4. Constrained optimization
1. Computer experiments
2. Surrogate models
3. Unconstrained optimization
11.
ＥＢ
by Iqbal Sarker, Alan Colman, Jun Han, Paul Watters
目次情報:
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Part I Preliminaries
1 Introduction to Context-Aware Machine Learning and Mobile Data
Analytics
1.1 Introduction
1.2 Context-Aware Machine Learning
1.3 Mobile Data Analytics
1.4 An Overview of this Book
1.5 Conclusion
References
2 Application Scenarios and Basic Structure for Context-Aware
Machine Learning Framework
2.1 Motivational Examples with Application Scenarios
2.2 Structure and Elements of Context-Aware Machine Learning
Framework
2.2.1 Contextual Data Acquisition
2.2.2 Context Discretization
2.2.3 Contextual Rule Discovery
2.2.4 Dynamic Updating and Management of Rules
2.3 Conclusion
3 A Literature Review on Context-Aware Machine Learning and
Mobile Data Analytics
3.1 Contextual Information
3.1.1 Definitions of Contexts
3.1.2 Understanding the Relevancy of Contexts
3.2 Context Discretization
3.2.1 Discretization of Time-Series Data
3.2.2 Static Segmentation
vii
viii Contents
3.2.3 Dynamic Segmentation
3.3 Rule Discovery
3.3.1 Association Rule Mining
3.3.2 Classification Rules
3.4 Incremental Learning and Updating
3.5 Identifying the Scope of Research
3.6 Conclusion
Part II Context-Aware Rule Learning and Management
4 Contextual Mobile Datasets, Pre-processing and Feature Selection
4.1 Smart Mobile Phone Data and Associated Contexts
4.1.1 Phone Call Log
4.1.2 Mobile SMS Log
4.1.3 Smartphone App Usage Log
4.1.4 Mobile Phone Notification Log
4.1.5 Web or Navigation Log
4.1.6 Game Log
4.1.7 Smartphone Life Log
4.1.8 Dataset Summary
4.2 Examples of Contextual Mobile Phone Data
4.2.1 Time-Series Mobile Phone Data
4.2.2 Mobile phone data with multi-dimensional contexts
4.2.3 Contextual Apps Usage Data
4.3 Data Preprocessing
4.3.1 Data Cleaning
4.3.2 Data Integration
4.3.3 Data Transformation
4.3.4 Data Reduction
4.4 Dimensionality Reduction
4.4.1 Feature Selection
4.4.2 Feature Extraction
4.4.3 Dimensionality Reduction Algorithms
4.5 Conclusion
5 Discretization of Time-Series Behavioral Data and Rule Generation
based on Temporal Context
5.1 Introduction
5.2 Requirements Analysis
5.3 Time-series Segmentation Approach
5.3.1 Approach Overview
5.3.2 Initial Time Slices Generation
5.3.3 Behavior-Oriented Segments Generation
Contents ix
5.3.4 Selection of Optimal Segmentation
5.3.5 Temporal Behavior Rule Generation using Time Segments
5.4 Effectiveness Comparison
5.5 Conclusion
6 Discovering User Behavioral Rules based on Multi-dimensional
Contexts
6.1 Introduction
6.2 Multi-dimensional Contexts in User Behavioral Rules
6.3 Requirements Analysis
6.4 Rule Mining Methodology
6.4.1 Identifying the Precedence of Context
6.4.2 Designing Association Generation Tree
6.4.3 Extracting Non-Redundant Behavioral Association Rules
6.5 Experimental Analysis
6.5.1 Effect on the Number of Produced Rules
6.5.2 Effect of Confidence Preference the Predicted Accuracy
6.5.3 Effectiveness Comparison
6.6 Conclusion
7 Recency-based Updating and Dynamic Management of Contextual
Rules
7.1 Introduction
7.2 Requirements Analysis
7.3 An Example of Recent Data
7.4 Identifying Optimal Period of Recent Log Data
7.4.1 Data Splitting
7.4.2 Association Generation
7.4.3 Score Calculation
7.4.4 Data Aggregation
7.5 Machine Learning based Behavioral Rule Generation and Management
7.6 Effectiveness Comparison and Analysis
7.7 Conclusion
Part III Application and Deep Learning Perspective
8 Context-Aware Rule-based Expert System Modeling
8.1 Structure of a Context-Aware Mobile Expert System
8.2 Context-Aware Rule Generation Methods
8.3 Context-Aware IF-THEN Rules and Discussion
8.3.1 IF-THEN Classification Rules
8.3.2 IF-THEN Association Rules
x Contents
8.4 Conclusion
9 Deep Learning for Contextual Mobile Data Analytics
9.1 Introduction
9.2 Contextual Data
9.3 Deep Neural Network Modeling
9.3.1 Model Overview
9.3.2 Input Layer
9.3.3 Hidden Layer(s)
9.3.4 Output Layer
9.4 Prediction Results of the Model
9.5 Conclusion
References -- 10 Context-Aware Machine Learning System: Applications and -- Challenging Issues -- 10.1 Rule-based Intelligent Mobile Applications -- 10.2 Major Challenges and Research Issues -- 10.3 Concluding Remarks -- References
Part I Preliminaries
1 Introduction to Context-Aware Machine Learning and Mobile Data
Analytics
12.
ＥＢ
edited by Simona Balzano, Giovanni C. Porzio, Renato Salvatore, Domenico Vistocco, Maurizio Vichi
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名:
Studies in Classification, Data Analysis, and Knowledge Organization ;
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https://doi.org/10.1007/978-3-030-69944-4
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Chapter 1 - Interpreting Effects in Generalized Linear Modeling (Alan Agresti, Claudia Tarantola, and Roberta Varriale)
Chapter 2 - ACE, AVAS and Robust Data Transformations: Performance of Investment Funds (Anthony C. Atkinson, Marco Riani, Aldo Corbellini, and Gianluca Morelli)
Chapter 3 - Predictive Principal Component Analysis (Simona Balzano, Maja Bozic, Laura Marcis, and Renato Salvatore)
Chapter 4 - Robust model-based learning to discover new wheat varieties and discriminate adulterated kernels in X-ray images (Andrea Cappozzo, Francesca Greselin, and Thomas Brendan Murphy)
Chapter 5 - A dynamic model for ordinal time series: an application to consumers’ perceptions of inflation (Marcella Corduas)
Chapter 6 - Deep learning to jointly analyze images and clinical data for disease detection (Federica Crobu and Agostino Di Ciaccio)
Chapter 7 -Studying Affiliation Networks through Cluster CA and Blockmodeling (Daniela D’Ambrosio, Marco Serino, and Giancarlo Ragozini)
Chapter 8 - Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles (Mauro D’Apuzzo, Rose-Line Spacagna, Azzurra Evangelisti, Daniela Santilli, and Vittorio Nicolosi)
Chapter 9 - Directional supervised learning through depth functions: an application to ECG waves analysis (Houyem Demni)
Chapter 10 - Penalized vs. contrained approaches for clusterwise linear regression modelling (Roberto Di Mari, Stefano Antonio Gattone, and Roberto Rocci)
Chapter 11 - Effect measures for group comparisons in a two-component mixture model: a cyber risk analysis (Maria Iannario and Claudia Tarantola)
Chapter 12 - A Cramér–von Mises test of uniformity on the hypersphere (Eduardo García-Portugués, Paula Navarro-Esteban, and Juan Antonio Cuesta-Albertos)
Chapter 13 - On mean and/or variance mixtures of normal distributions (Sharon X. Lee and Geoffrey J. McLachlan)
Chapter 14 - Robust depth-based inference in elliptical models (Stanislav Nagy and Jiří Dvořák)
Chapter 15 - Latent class analysis for the derivation of marketing decisions: An empirical study for BEV battery manufacturers (Friederike Paetz)
Chapter 16 - Small Area Estimation Diagnostics: the Case of the Fay-Herriot Model (Maria Chiara Pagliarella)
Chapter 17 - A comparison between methods to cluster mixed-type data: Gaussian mixtures versus Gower distance (Monia Ranalli and Roberto Rocci)
Chapter 18 - Exploring the gender gap in Erasmus student mobility flows (Marialuisa Restaino, Ilaria Primerano, and Maria Prosperina Vitale)
Chapter 1 - Interpreting Effects in Generalized Linear Modeling (Alan Agresti, Claudia Tarantola, and Roberta Varriale)
Chapter 2 - ACE, AVAS and Robust Data Transformations: Performance of Investment Funds (Anthony C. Atkinson, Marco Riani, Aldo Corbellini, and Gianluca Morelli)
Chapter 3 - Predictive Principal Component Analysis (Simona Balzano, Maja Bozic, Laura Marcis, and Renato Salvatore)
13.
ＥＢ
by Xudong Mao, Qing Li
目次情報:
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Generative Adversarial Networks (GANs)
GANs for Image Generation
More Key Applications of GANs
Conclusions
Generative Adversarial Networks (GANs)
GANs for Image Generation
More Key Applications of GANs
14.
ＥＢ
by Shan Liu, Min Zhang, Pranav Kadam, C.-C. Jay Kuo
目次情報:
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I. Introduction
II. Traditional point cloud analysis
III. Deep-learning-based point cloud analysis
IV. Explainable machine learning methods for point cloud analysis
V. Conclusion and future work
I. Introduction
II. Traditional point cloud analysis
III. Deep-learning-based point cloud analysis
15.
ＥＢ
by Patrick J. Laub, Young Lee, Thomas Taimre
目次情報:
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Background
Hawes Process Essentials
Simulation Methods
Likelihood Methods
EM Algorithm
Bayesian Methods
Spectral Methods
Goodness of Fit
Traditional Applications
Financial and Actuarial Applications
Biological Applications
Background
Hawes Process Essentials
Simulation Methods
16.
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by A.J. Larner
目次情報:
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Introduction
Paired measures
Paired complementary measures
Unitary measures
Reciprocal measures
Other measures, other tables
Outcome measures not directly related to the 2x2 table
Index
Introduction
Paired measures
Paired complementary measures
17.
ＥＢ
by Feng Bao
出版情報:
Singapore : Springer Nature Singapore : Imprint: Springer, 2021
シリーズ名:
Springer Theses, Recognizing Outstanding Ph.D. Research ;
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-981-16-3064-4
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Chapter 1 Introduction
Chapter 2 Fast computational recovery of missing features for large-scale biological data
Chapter 3 Computational recovery of information from low-quality and missing labels
Chapter 4 Computational recovery of sample missings
Chapter 5 Summary and outlook
Chapter 1 Introduction
Chapter 2 Fast computational recovery of missing features for large-scale biological data
Chapter 3 Computational recovery of information from low-quality and missing labels
18.
ＥＢ
edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名:
Springer Optimization and Its Applications ; 170
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https://doi.org/10.1007/978-3-030-66515-9
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Learning enabled constrained black box optimization (Archetti)
Black-box optimization: Methods and applications (Hasan)
Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein)
Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis)
Multi-objective evolutionary algorithms: Past, present and future (Coello C.A)
Black-box and data driven computation (Du)
Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott)
Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich)
Variable neighborhood programming as a tool of machine learning (Mladenovic)
Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky)
Finding effective SAT partitionings via black-box optimization (Semenov)
The No Free Lunch Theorem: What are its main implications for the optimization practice? ( Serafino)
What is important about the No Free Lunch theorems? (Wolpert)
Learning enabled constrained black box optimization (Archetti)
Black-box optimization: Methods and applications (Hasan)
Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein)
19.
ＥＢ
by Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Muhammad Zuhaili Suhaimi, Mohd Azraai Mohd Razman, Mohamad Razali Abdullah, Noor Azuan Abu Osman
出版情報:
Singapore : Springer Nature Singapore : Imprint: Springer, 2021
シリーズ名:
SpringerBriefs in Applied Sciences and Technology ;
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オンライン:
https://doi.org/10.1007/978-981-16-3192-4
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Chapter 1. Nature of Volleyball Sport, Performance Analysis in Volleyball, and the Recent Advances of Machine Learning Application in Sports
Chapter 2. The Effect of Competition strategies in influencing Volleyball performance
Chapter 3. Identification of psychological training strategies essential for Volleyball performance
Chapter 4. The Strategic competitional elements contributing to Volleyball performance
Chapter 5. Anthropometric variables in the identification of high-performance Volleyball players
Chapter 6. Performance Indicators predicting medalists and non-medalists in elite men Volleyball competition
Chapter 7. Summary, Conclusion and Future Direction
Chapter 1. Nature of Volleyball Sport, Performance Analysis in Volleyball, and the Recent Advances of Machine Learning Application in Sports
Chapter 2. The Effect of Competition strategies in influencing Volleyball performance
Chapter 3. Identification of psychological training strategies essential for Volleyball performance
20.
ＥＢ
by Askhat Diveev, Elizaveta Shmalko
目次情報:
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1.Introduction
2.Mathematical Statements of MLC Problems
3.Numerical Solution of Machine Learning Control Problems
4.Symbolic Regression Methods
5.Examples of MLC Problem Solutions
1.Introduction
2.Mathematical Statements of MLC Problems
3.Numerical Solution of Machine Learning Control Problems
21.
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by Yang-Hui He
22.
ＥＢ
edited by Martin Werner, Yao-Yi Chiang
目次情報:
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I Introduction
II Spatial Big Data Platforms & Infrastructures
III Spatial Data Acquisition
IV Indexing and Retrieval of Spatial Big Data
V Scalable Algorithms for Spatial Analytics
VI Data Mining, Machine Learning and Artificial Intelligence
VII Visualization & Interaction
VIII Applications
I Introduction
II Spatial Big Data Platforms & Infrastructures
III Spatial Data Acquisition
23.
ＥＢ
by Samuli Siltanen
目次情報:
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Foreword: Mathematics, our invisible friend
1. My adventures in the world of mathematics
2. Examples of mathematical models
3. The large models of Earth and space
4. Mathematics in the service of doctors
5. Is humanity mathematics as well?
Mathematics belongs to everyone
Foreword: Mathematics, our invisible friend
1. My adventures in the world of mathematics
2. Examples of mathematical models
24.
ＥＢ
by Aygul Zagidullina
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名:
SpringerBriefs in Applied Statistics and Econometrics ;
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オンライン:
https://doi.org/10.1007/978-3-030-80065-9
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Foreword
1 Introduction
2 Traditional Estimators and Standard Asymptotics
3 Finite Sample Performance of Traditional Estimators
4 Traditional Estimators and High-Dimensional Asymptotics
5 Summary and Outlook
Appendices
Foreword
1 Introduction
2 Traditional Estimators and Standard Asymptotics
25.
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by Sylvain Lespinats, Benoit Colange, Denys Dutykh
目次情報:
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1 Data science context
1.1 Data in a metric space
1.1.1 Measuring dissimilarities and similarities
1.1.2 Neighbourhood ranks
1.1.3 Embedding space notations
1.1.4 Multidimensional data
1.1.5 Sequence data
1.1.6 Network data
1.1.7 A few multidimensional datasets
1.2 Automated tasks
1.2.1 Underlying distribution
1.2.2 Category identification
1.2.3 Data manifold analysis
1.2.4 Model learning
1.2.5 Regression
1.3 Visual exploration
1.3.1 Human in the loop using graphic variables
1.3.2 Spatialization and Gestalt principles
1.3.3 Scatter plots
1.3.4 Parallel coordinates
1.3.5 Colour coding
1.3.6 Multiple coordinated views and visual interaction
1.3.7 Graph drawing
2 Intrinsic dimensionality
2.1 Curse of dimensionality
2.1.1 Data sparsity
2.1.2 Norm concentration
2.2 ID estimation
2.2.1 Covariance-based approaches
2.2.2 Fractal approaches
2.2.3 Towards local estimation
2.3 TIDLE
2.3.1 Gaussian mixture modelling
2.3.2 Test of TIDLE on a two clusters case
3 Map evaluation
3.1 Objective and practical indicators
3.1.1 Subjectivity of indicators
3.1.2 User studies on specific tasks
3.2 Unsupervised global evaluation
3.2.1 Types of distortions
3.2.2 Link between distortions and mapping continuity
3.2.3 Reasons of distortions ubiquity
3.2.4 Scalar indicators
3.2.5 Aggregation
3.2.6 Diagrams
3.3 Class-aware indicators
3.3.1 Class separation and aggregation
3.3.2 Comparing scores between the two spaces
3.3.3 Class cohesion and distinction
3.3.4 The case of one cluster per class
4 Map interpretation
4.1 Axes recovery
4.1.1 Linear case: biplots
4.1.2 Non-linear case
4.2 Local evaluation
4.2.1 Point-wise aggregation
4.2.2 One to many relations with focus point
4.2.3 Many to many relations
4.3 MING
4.3.1 Uniform formulation of rank-based indicator
4.3.2 MING graphs
4.3.3 MING analysis for a toy dataset
4.3.4 Impact of MING parameters
4.3.5 Visual clutter
4.3.6 Oil flow
4.3.7 COIL-20 dataset
4.3.8 MING perspectives
5 Unsupervised DR
5.1 Spectral projections
5.1.1 Principal Component Analysis
5.1.2 Classical MultiDimensional Scaling
5.1.3 Kernel methods: Isompap, KPCA, LE
5.2 Non-linear MDS
5.2.1 Metric MultiDimensional Scaling
5.2.2 Non-metric MultiDimensional Scaling
5.3 Neighbourhood Embedding
5.3.1 General principle: SNE
5.3.2 Scale setting
5.3.3 Divergence choice: NeRV and JSE
5.3.4 Symmetrization
5.3.5 Solving the crowding problem: tSNE
5.3.6 Kernel choice
5.3.7 Adaptive Student Kernel Imbedding
5.4 Graph layout
5.4.1 Force directed graph layout: Elastic Embedding
5.4.2 Probabilistic graph layout: LargeVis
5.4.3 Topological method UMAP
5.5 Artificial neural networks
5.5.1 Auto-encoders
5.5.2 IVIS
6 Supervised DR
6.1 Types of supervision
6.1.1 Full supervision
6.1.2 Weak supervision
6.1.3 Semi-supervision
6.2 Parametric with class purity
6.2.1 Linear Discriminant Analysis
6.2.2 Neighbourhood Component Analysis
6.3 Metric learning
6.3.1 Mahalanobis distances
6.3.2 Riemannian metric
6.3.3 Direct distances transformation
6.3.4 Similarities learning
6.3.5 Metric learning limitations
6.4 Class adaptive scale
6.5 Classimap
6.6 CGNE
6.6.1 ClassNeRV stress
6.6.2 Flexibility of the supervision
6.6.3 Ablation study
6.6.4 Isolet 5 case study
6.6.5 Robustness to class misinformation
6.6.6 Extension to the type 2 mixture: ClassJSE
6.6.7 Extension to semi-supervision and weak-supervision
6.6.8 Extension to soft labels
7 Mapping construction
7.1 Optimization
7.1.1 Global and local optima
7.1.2 Descent algorithms
7.1.3 Initialization
7.1.4 Multi-scale optimization
7.1.5 Force-directed placement interpretation
7.2 Acceleration strategies
7.2.1 Attractive forces approximation
7.2.2 Binary search trees
7.2.3 Repulsive forces
7.2.4 Landmarks approximation
7.3 Out of sample extension
1 Data science context
1.1 Data in a metric space
1.1.1 Measuring dissimilarities and similarities
26.
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by Mayer Alvo
目次情報:
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I. Introduction to Big Data
Examples of Big Data
II. Statistical Inference for Big Data
Basic Concepts in Probability
Basic Concepts in Statistics
Multivariate Methods
Nonparametric Statistics
Exponential Tilting and its Applications
Counting Data Analysis
Time Series Methods
Estimating Equations
Symbolic Data Analysis
III Machine Learning for Big Data
Tools for Machine Learning
Neural Networks
IV Computational Methods for Statistical Inference
Bayesian Computation Methods
I. Introduction to Big Data
Examples of Big Data
II. Statistical Inference for Big Data
27.
ＥＢ
by Fuwei Li, Lifeng Lai, Shuguang Cui
目次情報:
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Chapter. 1. Introduction
Chapter. 2. Optimal Feature Manipulation Attacks Against Linear Regression
Chapter. 3. On the Adversarial Robustness of LASSO Based Feature Selection
Chapter. 4. On the Adversarial Robustness of Subspace Learning
Chapter. 5. Summary and Extensions
Chapter. 6. Appendix
Chapter. 1. Introduction
Chapter. 2. Optimal Feature Manipulation Attacks Against Linear Regression
Chapter. 3. On the Adversarial Robustness of LASSO Based Feature Selection
28.
ＥＢ
by Christo El Morr, Manar Jammal, Hossam Ali-Hassan, Walid EI-Hallak
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名:
International Series in Operations Research & Management Science ; 334
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-3-031-16990-8
所蔵情報:
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目次情報:
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1. Introduction to Machine Learning
2. Statistics
3. Overview of Machine Learning Algorithms
4. Data Preprocessing
5. Data Visualization
6. Linear Regression
7. Logistic Regression
8. Decision Trees
9. Naïve Bayes
10. K-Nearest Neighbors
11. Neural Networks
12. K-Means
13. Support Vector Machine
14. Voting and Bagging
15. Boosting and Stacking
16. Future Directions and Ethical Considerations
1. Introduction to Machine Learning
2. Statistics
3. Overview of Machine Learning Algorithms
29.
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by Robert Ball, Brian Rague
目次情報:
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Chapter. 1. Introduction to Data Science
Chapter. 2. Data Collection
Chapter. 3. Data Wrangling
Chapter. 4. Crash Course on Descriptive Statistics
Chapter. 5. Inferential Statistics
Chapter. 6. Metrics
Chapter. 7. Recommendation Engines
Chapter. 8. Machine Learning
Chapter. 9
Natural Language Processing (NLP)
Chapter. 10. Time Series
Chapter. 11. Final Product
Chapter. 1. Introduction to Data Science
Chapter. 2. Data Collection
Chapter. 3. Data Wrangling
30.
ＥＢ
by Kojiro Shojima
出版情報:
Singapore : Springer Nature Singapore : Imprint: Springer, 2022
シリーズ名:
Behaviormetrics: Quantitative Approaches to Human Behavior ; 13
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-981-16-9986-3
所蔵情報:
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Concept of Test Data Engineering
Test Data and Item Analysis
Classical Test Theory
Item Response Theory
Latent Class Analysis
Biclustering
Bayesian Network Model
Concept of Test Data Engineering
Test Data and Item Analysis
Classical Test Theory
31.
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by Vladimir Vovk, Alexander Gammerman, Glenn Shafer
目次情報:
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1. Introduction
Part I Set prediction
2. Conformal prediction: general case and regression
3. Conformal prediction: classification and general case
4. Modifications of conformal predictors
Part II Probabilistic prediction
5. Impossibility results
6. Probabilistic classification: Venn predictors
7. Probabilistic regression: conformal predictive systems
Part III Testing randomness
8. Testing exchangeability
9. Efficiency of conformal testing
10. Non-conformal shortcut
Part IV Online compression modelling
11. Generalized conformal prediction
12. Generalized Venn prediction and hypergraphical models
13. Contrasts and perspectives
1. Introduction
Part I Set prediction
2. Conformal prediction: general case and regression
32.
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by Alfio Quarteroni
目次情報:
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1 Epidemic
2 Retrospective
3 Interlude: the revolution that did not happen and the revolution that was unforeseen
4 Artificial intelligence, learning computers, artificial neural networks
5 A bit of maths (behind artificial intelligence and machine learning)
6 BIG DATA - BIG BROTHER (or, on the ethical and moral aspects of artificial intelligence)
1 Epidemic
2 Retrospective
3 Interlude: the revolution that did not happen and the revolution that was unforeseen
33.
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edited by Panos M. Pardalos, Stamatina Th. Rassia, Arsenios Tsokas
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名:
Springer Optimization and Its Applications ; 186
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-3-030-84459-2
所蔵情報:
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Cities as Convergent Autopoietic Systems (V. Dobrev)
Digital ‘Vitalism’ and its ‘Epistemic’ Predecessors: ‘Smart’ Neoteric History and Contemporary Approaches (Moraitis)
Unbuildable Cities (Th. Rassia)
Smart Cities as Identities (Tsoniotis)
A Cross-domain Landscape of ICT Services in Smart Cities (Bunhova)
A Novel Data Representation Method for Smart Cities’ Big Data (N. Nagy)
A Pedestrian Level Strategy to Minimize Outdoor Sunlight Exposure (X. Li)
Planning and Management of Charging Facilities for Electric Vehicle Sharing (W. Qi)
A Reactive Architectural Proposal for Fog/edge Computing in the Internet of Things Paradigm with Application in Deep Learning (Belmonte-Fernandez)
Urban Big Data: City Management and Real Estate Markets (Saiz)
Social Media-based Intelligence for Disaster Response and Management in Smart Cities (Khatoon)
Cities as Convergent Autopoietic Systems (V. Dobrev)
Digital ‘Vitalism’ and its ‘Epistemic’ Predecessors: ‘Smart’ Neoteric History and Contemporary Approaches (Moraitis)
Unbuildable Cities (Th. Rassia)
34.
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by Sheetal S. Sonawane, Parikshit N. Mahalle, Archana S. Ghotkar
目次情報:
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Part A
Chapter 1. Graph theory basics
Chapter 2. Graph Algorithms
Chapter 3. Networks using graph
Part B
Chapter 4. Information retrieval
Chapter 5. Text document preprocessing using graph theory
Chapter 6. Text analytics using graph theory
Chapter 7. Knowledge graph
Part C
Chapter 8. Emerging Applications and development
Chapter 9. Conclusion and future scope
Part A
Chapter 1. Graph theory basics
Chapter 2. Graph Algorithms
35.
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by Uwe Lorenz
目次情報:
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1 Reinforcement learning as subfield of machine learning
2 Basic concepts of reinforcement learning
3 Optimal decision-making in a known environment
4 decision making and learning in an unknown environment
5 Artificial Neural Networks as estimators for state values and the action selection
6 Guiding ideas in Artificial Intelligence over time
1 Reinforcement learning as subfield of machine learning
2 Basic concepts of reinforcement learning
3 Optimal decision-making in a known environment
36.
ＥＢ
edited by Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng
目次情報:
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1. What is Data Science (DS)?
2. Solutions to Data Science Problems
3. What is Dimensionality Reduction (DR)?
4. Conventional Statistical Approaches
5. Geometric Approaches
6. Information-theoretic Approaches
7. Molecular Computing Approaches
8. Statistical Learning Approaches
9. Machine Learning Approaches
10. Metaheuristics of DR Methods
11. Appendices
1. What is Data Science (DS)?
2. Solutions to Data Science Problems
3. What is Dimensionality Reduction (DR)?
37.
ＥＢ
edited by Syeda Darakhshan Jabeen, Javid Ali, Oscar Castillo
出版情報:
Singapore : Springer Nature Singapore : Imprint: Springer, 2022
シリーズ名:
Springer Proceedings in Mathematics & Statistics ; 404
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-981-19-6406-0
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R. Kumari, A. Nigam and S. Pushkar, Classification of MRI images for detecting Alzheimer Disease using Convolutional Neural Network
S. D. Godwal and S. S. Kanojia, Optimum over Current Relays Coordination for Radial Distribution Networks Using Soft-Computing Techniques
Priyavada and B. Kumar, A Review of Modified Particle Swarm Optimization Method
D. Patel and S. Sharma Automated Detection of Elephant Using AI Techniques
P. Rajak and P. Roy, Determination of Probability of Failure of Structures Using DBSCAN and Support Vector Machine
Md. Nayer and S. C. Pandey, The Ensemble of Ant Colony Optimization and Gradient Descent Technique for Efficient Feature Selection and Data Classification
A. K. Prasad and S. S. Thakur, s-Regularity Via Soft Ideal
K. A. Pattani and S. Gautam, A Comprehensive Study on Mobile Malwares: Mobile Covert Channels - Threats and Security
F. Nikbakhtsarvestani, Overview of Incorporating Nonlinear Functions into Recurrent Neural Network Models
Sangita A. Jaju, Sudhir B. Jagtap, and R. Shinde, A Soft Computing Approach for Predicting and Categorising Learner’s Performance using Fuzzy Model
T. Kaur Bhatia, A. Kumar, M.K. Sharma, and S.S. Appadoo, A Fuzzy Logic based Approach to Solve Interval Multiobjective Nonlinear Transportation Problem: Suggested Modifications
O. Castillo and P. Melin, Interval Type-3 Fuzzy Decision Making in Material Surface Quality Control
F. A. Khan, J. Ali, Fatimah N. Albishi, and F. Gursoy, A Generalized Nonlinear Quasi-Variational-Like Inclusion Problem Involving Fuzzy Mappings
B. Kohli, Approximate Optimality Conditions via Convexifactors for Multiobjective Programming Problems
F. Mirdamadi, H. Monfared, Mehdi Asadi, and H. Soleimani, A Remark on Discontinuity of Fixed Point on Partial Metric Spaces
A. Priya, A. Kumari and M. Singh, Implementation of Fuzzy Logic in Home Appliances
S. Banerjee, B. Guha, A. Ghosh, and G. Bandyopadhyay, Portfolio Structure of Debt Mutual Funds in Indian Market
R. Om Gayathri and R. Hemavathy, Fixed Point Theorems for Digital Images using Path Length Metric
R. P. Verma, N. Kumar, S. Kumar, and Sruthi S., Emergency Help for Road Accidents
E. Arul, S. Akash Kumar, R. Ragul, and Yuvaanesh V.B., Music Classification Based on Lyrics and Audio by using Machine Learning
S. Khalil and U. M. Modibbo, Multiobjective Optimization for Hospital Nurse Scheduling Problem
Z. Ghouli, On the Performance of a Flow Energy Harvester using Time Delay
A. Badran, Identification of Some Spatial Coefficients in Some Engineering Topics
U. Bhagirathi and R. Ajoodha, Automatic Venue Allocation for Varying Class Sizes using Scoring and Heuristic Hill-climbing
N. K. Sharma, S. Kumar, A. Rajpal, and N. Kumar, DWT and Quantization based Digital Watermarking Scheme using Kernel OS-ELM
P. Muralikrishna, P. Hemavathi, and K. Palanivel, Stress Level Analysis Using Bipolar Picture Fuzzy Set
Y. Kimura and K. Shindo, Asymptotic Behavior of Resolvents on Complete Geodesic Spaces with General Perturbation Functions
R. Kumari, A. Nigam and S. Pushkar, Classification of MRI images for detecting Alzheimer Disease using Convolutional Neural Network
S. D. Godwal and S. S. Kanojia, Optimum over Current Relays Coordination for Radial Distribution Networks Using Soft-Computing Techniques
Priyavada and B. Kumar, A Review of Modified Particle Swarm Optimization Method
38.
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by Leonhard Kunczik
目次情報:
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Motivation: Complex Attacker-Defender Scenarios - The eternal conflict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman’s Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing
Approximation in Quantum Computing
Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning
Applying Quantum REINFORCE to the Information Game
Evaluating quantum REINFORCE on IBM’s Quantum Hardware
Future Steps in Quantum Reinforcement Learning for Complex Scenarios
Conclusion
Motivation: Complex Attacker-Defender Scenarios - The eternal conflict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman’s Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing
Approximation in Quantum Computing
Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning
39.
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by Johannes Lederer
目次情報:
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Preface
Notation
Introduction
Linear Regression
Graphical Models
Tuning-Parameter Calibration
Inference
Theory I: Prediction
Theory II: Estimation and Support Recovery
A Solutions
B Mathematical Background
Bibliography
Index
Preface
Notation
Introduction
40.
ＥＢ
by Schirin Bär
目次情報:
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Introduction
Requirements for Production Scheduling in Flexible Manufacturing
Reinforcement Learning as an Approach for Flexible Scheduling
Concept for Multi-Resources Flexible Job-Shop Scheduling
Multi-Agent Approach for Reactive Scheduling in Flexible Manufacturing
Empirical Evaluation of the Requirements
Integration into a Flexible Manufacturing System
Bibliography
Introduction
Requirements for Production Scheduling in Flexible Manufacturing
Reinforcement Learning as an Approach for Flexible Scheduling
41.
ＥＢ
by Masanori Hanada, So Matsuura
目次情報:
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Chapter 1: Introduction
Chapter 2: What is the Monte Carlo method?
Chapter 3: General Aspects of Markov Chain Monte Carlo
Chapter 4: Metropolis Algorithm
Chapter 5: Other Useful Algorithms
Chapter 6: Applications of Markov Chain Monte Carlo
Chapter 1: Introduction
Chapter 2: What is the Monte Carlo method?
Chapter 3: General Aspects of Markov Chain Monte Carlo
42.
ＥＢ
edited by Ansgar Steland, Kwok-Leung Tsui
43.
ＥＢ
by Sergei Pereverzyev
目次情報:
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Introduction
Learning in Reproducing Kernel Hilbert Spaces and related integral operators
Selected topics of the regularization theory
Regularized learning in RKHS
Examples of Applications
Introduction
Learning in Reproducing Kernel Hilbert Spaces and related integral operators
Selected topics of the regularization theory
44.
ＥＢ
by Changquan Huang, Alla Petukhina
目次情報:
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1. Time Series Concepts and Python
2. Exploratory Time Series Data Analysis
3. Stationary Time Series Models
4. ARMA and ARIMA Modeling and Forecasting
5. Nonstationary Time Series Models
6. Financial Time Series and Related Models
7. Multivariate Time Series Analysis
8. State Space Models and Markov Switching Models
9. Nonstationarity and Cointegrations
10. Modern Machine Learning Methods for Time Series Analysis
1. Time Series Concepts and Python
2. Exploratory Time Series Data Analysis
3. Stationary Time Series Models
45.
ＥＢ
by David Ramírez, Ignacio Santamaría, Louis Scharf
目次情報:
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Introduction
Historical perspective, motivating problems, and preview of what is to come
Least Squares and related
Classical correlations and coherence
Coherence in the multivariate normal (MVN) model
Classical tests for correlation
One-channel matched subspace detectors
Adaptive subspace detectors
Two channel matched subspace detectors
Detection of spatially-correlated time series
Coherence and the detection of cyclostationarity
Partial coherence for testing causality
Subspace averaging
Coherence and performance bounds
Variations on coherence
Conclusion
Introduction
Historical perspective, motivating problems, and preview of what is to come
Least Squares and related
46.
ＥＢ
edited by Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos
出版情報:
Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名:
Springer Optimization and Its Applications ; 199
子書誌情報:
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オンライン:
https://doi.org/10.1007/978-3-031-21225-3
所蔵情報:
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目次情報:
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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)
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)
47.
ＥＢ
edited by Vinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin
目次情報:
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Importance of machine learning in material science
Machine Learning: A methodology to explain and predict material behavior
Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network
Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites
Forward machine learning technique to predict dynamic fracture behavior of particulate composite
Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates
Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates
Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning
Effect of natural fiber’s mechanical properties and fiber matrix adhesion strength to design biocomposite
Comparison of various machine learning algorithms to predict material behavior in GFRP
Importance of machine learning in material science
Machine Learning: A methodology to explain and predict material behavior
Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network
48.
ＥＢ
edited by Wenqing He, Liqun Wang, Jiahua Chen, Chunfang Devon Lin
目次情報:
続きを見る
1. MiRNA-Gene Activity Interaction Networks (miGAIn): Integrated joint models of miRNA-gene targeting and disturbance in signal processing
2. Feature Screening for Ultrahigh-Dimensional Regression with Error-Prone Varables
3. Cosine Distribution in the Post-selection Inference of Least Angle Regression
4. Learning Finite Gaussian Mixture via Wasserstein Distance
5. An Entropy-based Method with Word Embedding Clustering for Comment Ranking
6. Estimation in Functional Linear Model with Incomplete Functional Observations
7. A Flexible Linear Single Index Proportional Hazards Regression Model for Multivariate Survival Data
8. Efficient Estimation of Semiparametric Linear Transformation Model with Left-Truncated and Current Status Data
9. Flexible Transformations for Modeling Compositional Data
10. Identifiability and Estimation of Autoregressive ARCH Models with Measurement Error
11. Modal Regression for Skewed, Truncated, or Contaminated Data with Outliers
12. Spatial Multilevel Modeling in the Galveston Bay Recovery Study Survey
13. Efficient Experimental Design for Regularized Linear Models
14. A Selective Overview of Statistical Models for Identification of Treatment-sensitive Subset
15. Analysis of Discrete Compositional Series While Accounting for Informative Time-dependent Cluster Sizes with Application to Air Pollution Related Emergency Room Visits
1. MiRNA-Gene Activity Interaction Networks (miGAIn): Integrated joint models of miRNA-gene targeting and disturbance in signal processing
2. Feature Screening for Ultrahigh-Dimensional Regression with Error-Prone Varables
3. Cosine Distribution in the Post-selection Inference of Least Angle Regression
ＥＢ
