فهرست مطالب

• Cover
• Table of Contents
• Title Page
• Copyright
• Dedication
• Preface
• About the Companion Website
• Chapter 1. Introduction
• 1.1 Defining Outliers
• 1.2 Overview of the Book
• 1.3 What Is Robust Machine Learning?
• 1.4 Robustness of the Median
• 1.5 and Norms
• 1.6 Review of Gaussian Distribution
• 1.7 Unsupervised Learning Case Study
• 1.8 Creating Synthetic Data for Clustering
• 1.9 Clustering Algorithms
• 1.10 Importance of Robust Clustering
• 1.11 Summary
• Problems
• References
• Notes
• Chapter 2. Robust Linear Regression
• 2.1 Introduction
• 2.2 Supervised Learning
• 2.3 Linear Regression
• 2.4 Importance of Residuals
• 2.5 Estimation Background
• 2.6 M‐Estimation
• 2.7 Least Squares Estimation (LSE)
• 2.8 Least Absolute Deviation (LAD)
• 2.9 Comparison of LSE and LAD
• 2.10 Huber's Method
• 2.11 Summary
• Problems
• References
• Chapter 3. The Log‐Cosh Loss Function
• 3.1 Introduction
• 3.2 An Intuitive View of Log‐Cosh
• 3.3 Hyperbolic Functions
• 3.4 M‐Estimation
• 3.5 Deriving the Distribution for Log‐Cosh
• 3.6 Standard Errors for Robust Estimators
• 3.7 Statistical Properties of Log‐Cosh Loss
• 3.8 A General Log‐Cosh Loss Function
• 3.9 Summary
• Problems
• References
• Notes
• Chapter 4. Outlier Detection, Metrics, and Standardization
• 4.1 Introduction
• 4.2 Effect of Outliers
• 4.3 Outlier Diagnosis
• 4.4 Outlier Detection
• 4.5 Outlier Removal
• 4.6 Regression‐Based Outlier Detection
• 4.7 Regression‐Based Outlier Removal
• 4.8 Regression Metrics with Outliers
• 4.9 Dataset Standardization
• 4.10 Summary
• Problems
• References
• Notes
• Chapter 5. Robustness of Penalty Estimators
• 5.1 Introduction
• 5.2 Penalty Functions
• 5.3 Ridge Penalty
• 5.4 LASSO Penalty
• 5.5 Effect of Penalty Functions
• 5.6 Penalty Functions with Outliers
• 5.7 Ridge Traces
• 5.8 Elastic Net (Enet) Penalty
• 5.9 Adaptive LASSO (aLASSO) Penalty
• 5.10 Penalty Effects on Variance and Bias
• 5.11 Variable Importance
• 5.12 Summary
• Problems
• References
• Notes
• Chapter 6. Robust Regularized Models
• 6.1 Introduction
• 6.2 Overfitting and Underfitting
• 6.3 The Bias–Variance Trade‐Off
• 6.4 Regularization with Ridge
• 6.5 Generalization using Robust Estimators
• 6.6 Robust Generalization and Regularization
• 6.7 Model Complexity
• 6.8 Summary
• Problems
• References
• Notes
• Chapter 7. Quantile Regression Using Log‐Cosh
• 7.1 Introduction
• 7.2 Understanding Quantile Regression
• 7.3 The Crossing Problem
• 7.4 Standard Quantile Loss Function
• 7.5 Smooth Regression Quantiles (SMRQ)
• 7.6 Evaluation of Quantile Methods
• 7.7 Selection of Robustness Coefficient
• 7.8 Maximum‐Likelihood Procedure for SMRQ
• 7.9 Standard Error Computation
• 7.10 Summary
• Problems
• References
• Chapter 8. Robust Binary Classification
• 8.1 Introduction
• 8.2 Binary Classification Problem
• 8.3 The Cross‐Entropy (CE) Loss
• 8.4 The Log‐Cosh (LC) Loss Function
• 8.5 Algorithms for Logistic Regression
• 8.6 Example: Motor Trend Cars
• 8.7 Regularization of Logistic Regression
• 8.8 Example: Circular Dataset
• 8.9 Outlier Detection
• 8.10 Robustness of Binary Classifiers
• 8.11 Summary
• Problems
• Reference
• Notes
• Chapter 9. Neural Networks Using Log‐Cosh
• 9.1 Introduction
• 9.2 A Brief History of Neural Networks
• 9.3 Defining Neural Networks
• 9.4 Training of Neural Networks
• 9.5 Forward and Backward Propagation
• 9.6 Cross‐entropy and Log‐Cosh Algorithms
• 9.7 Example: Circular Dataset
• 9.8 Classification Metrics and Outliers
• 9.9 Summary
• Problems
• References
• Notes
• Chapter 10. Multi‐class Classification and Adam Optimization
• 10.1 Introduction
• 10.2 Multi‐class Classification
• 10.3 Example: MNIST Dataset
• 10.4 Optimization of Neural Networks
• 10.5 Summary
• Problems
• References
• Notes
• Chapter 11. Anomaly Detection and Evaluation Metrics
• 11.1 Introduction
• 11.2 Anomaly Detection Methods
• 11.3 Anomaly Detection Using MADmax
• 11.4 Qualitative Evaluation Methods
• 11.5 Quantitative Evaluation Methods
• 11.6 Summary
• Problems
• Reference
• Notes
• Chapter 12. Case Studies in Data Science
• 12.1 Introduction
• 12.2 Example: Boston Housing Dataset
• 12.3 Example: Titanic Dataset
• 12.4 Application to Explainable Artificial Intelligence (XAI)
• 12.5 Time Series Example: Climate Change
• 12.6 Summary and Conclusions
• Problems
• References
• Notes
• Index
• End User License Agreement