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Savo G. Glisic
Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 1
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Вернуться на страницу книги Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
Оглавление
Страница 1
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
Страница 8
Preface
Страница 10
1 Introduction 1.1 Motivation
1.2 Book Structure
References
2 Machine Learning Algorithms
2.1 Fundamentals 2.1.1 Linear Regression
2.1.2 Logistic Regression
2.1.3 Decision Tree: Regression Trees Versus Classification Trees
2.1.4 Trees in R and Python
2.1.5 Bagging and Random Forest
2.1.6 Boosting GBM and XGBoost
2.1.7 Support Vector Machine
2.1.8 Naive Bayes, kNN, k‐Means
Example 2.1
Design Example 2.1
Example 2.2
Example 2.3
Example 2.4
2.1.9 Dimensionality Reduction
Design Example 2.2
2.2 ML Algorithm Analysis 2.2.1 Logistic Regression
2.2.2 Decision Tree Classifiers
Design Example 2.3
Design Example 2.4
2.2.3 Dimensionality Reduction Techniques
Design Example 2.5
References
3 Artificial Neural Networks 3.1 Multi‐layer Feedforward Neural Networks 3.1.1 Single Neurons
3.1.2 Weights Optimization
3.2 FIR Architecture 3.2.1 Spatial Temporal Representations
3.2.2 Neural Network Unfolding
Example
3.2.3 Adaptation
Design Example 3.1
3.3 Time Series Prediction
3.3.1 Adaptation and Iterated Predictions
3.4 Recurrent Neural Networks 3.4.1 Filters as Predictors
3.4.2 Feedback Options in Recurrent Neural Networks
3.4.3 Advanced RNN Architectures
3.5 Cellular Neural Networks (CeNN)
3.6 Convolutional Neural Network (CoNN)
3.6.1 CoNN Architecture
3.6.2 Layers in CoNN
References
4 Explainable Neural Networks
4.1 Explainability Methods
4.1.1 The Complexity and Interoperability
4.1.2 Global Versus Local Interpretability
4.1.3 Model Extraction
4.2 Relevance Propagation in ANN
4.2.1 Pixel‐wise Decomposition
4.2.2 Pixel‐wise Decomposition for Multilayer NN
4.3 Rule Extraction from LSTM Networks
4.4 Accuracy and Interpretability
4.4.1 Fuzzy Models
Design Example 4.1
Design Example 4.2
Design Example 4.3
Algorithm 4.1
Mamdani (max‐min) inference
Design Example 4.4
Design Example 4.5
4.4.2 SVR
4.4.3 Combination of Fuzzy Models and SVR
References
5 Graph Neural Networks
5.1 Concept of Graph Neural Network (GNN)
5.1.1 Classification of Graphs
5.1.2 Propagation Types
5.1.3 Graph Networks
5.2 Categorization and Modeling of GNN
5.2.1 RecGNNs
5.2.2
ConvGNNs
5.2.3 Graph Autoencoders (GAEs)
5.2.4 STGNNs
5.3 Complexity of NN
5.3.1 Labeled Graph NN (LGNN)
Theorem 5.1
Theorem 5.2
5.3.2 Computational Complexity
Appendix 5.A Notes on Graph Laplacian
Proposition 5.1
Proposition 5.2
Appendix 5.B Graph Fourier Transform
References
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