Inference and Learning from Data: Volume 3: Learning 1st Edition by Ali H. Sayed 9781009218306 1009218301

Inference and Learning from Data: Volume 3: Learning 1st Edition Ali H. Sayed – Ebook Instant Download/Delivery ISBN(s): 9781009218306, 1009218301

Product details:

• ISBN 10:1009218301
• ISBN 13:97810092183
• Author: Ali H. Sayed

Inference and Learning from Data: Volume 3
Learning

Table contents:

Preface
P.1 Emphasis on Foundations
P.2 Glimpse of History
P.3 Organization of the Text
P.4 How to Use the Text
P.5 Simulation Datasets
P.6 Acknowledgments
Notation
50 Least-Squares Problems
50.1 Motivation
50.2 Normal Equations
50.3 Recursive Least-Squares
50.4 Implicit Bias
50.5 Commentaries and Discussion
Problems
50.A Minimum-Norm Solution
50.B Equivalence in Linear Estimation
50.C Extended Least-Squares
References
51 Regularization
51.1 Three Challenges
51.2 [ell[sub(2)]]-Regularization
51.3 [ell[sub(1)]]-Regularization
51.4 Soft Thresholding
51.5 Commentaries and Discussion
Problems
51.A Constrained Formulations for Regularization
51.B Expression for LASSO Solution
References
52 Nearest-Neighbor Rule
52.1 Bayes Classifier
52.2 k-NN Classifier
52.3 Performance Guarantee
52.4 k-Means Algorithm
52.5 Commentaries and Discussion
Problems
52.A Performance of the NN Classifier
References
53 Self-Organizing Maps
53.1 Grid Arrangements
53.2 Training Algorithm
53.3 Visualization
53.4 Commentaries and Discussion
Problems
References
54 Decision Trees
54.1 Trees and Attributes
54.2 Selecting Attributes
54.3 Constructing a Tree
54.4 Commentaries and Discussion
Problems
References
55 Naïve Bayes Classifier
55.1 Independence Condition
55.2 Modeling the Conditional Distribution
55.3 Estimating the Priors
55.4 Gaussian Naïve Classifier
55.5 Commentaries and Discussion
Problems
References
56 Linear Discriminant Analysis
56.1 Discriminant Functions
56.2 Linear Discriminant Algorithm
56.3 Minimum Distance Classifier
56.4 Fisher Discriminant Analysis
56.5 Commentaries and Discussion
Problems
References
57 Principal Component Analysis
57.1 Data Preprocessing
57.2 Dimensionality Reduction
57.3 Subspace Interpretations
57.4 Sparse PCA
57.5 Probabilistic PCA
57.6 Commentaries and Discussion
Problems
57.A Maximum-Likelihood Solution
57.B Alternative Optimization Problem
References
58 Dictionary Learning
58.1 Learning Under Regularization
58.2 Learning Under Constraints
58.3 K-SVD Approach
58.4 Nonnegative Matrix Factorization
58.5 Commentaries and Discussion
Problems
58.A Orthogonal Matching Pursuit
References
59 Logistic Regression
59.1 Logistic Model
59.2 Logistic Empirical Risk
59.3 Multiclass Classification
59.4 Active Learning
59.5 Domain Adaptation
59.6 Commentaries and Discussion
Problems
59.A Generalized Linear Models
References
60 Perceptron
60.1 Linear Separability
60.2 Perceptron Empirical Risk
60.3 Termination in Finite Steps
60.4 Pocket Perceptron
60.5 Commentaries and Discussion
Problems
60.A Counting Theorem
60.B Boolean Functions
References
61 Support Vector Machines
61.1 SVM Empirical Risk
61.2 Convex Quadratic Program
61.3 Cross Validation
61.4 Commentaries and Discussion
Problems
References
62 Bagging and Boosting
62.1 Bagging Classifiers
62.2 AdaBoost Classifier
62.3 Gradient Boosting
62.4 Commentaries and Discussion
Problems
References
63 Kernel Methods
63.1 Motivation
63.2 Nonlinear Mappings
63.3 Polynomial and Gaussian Kernels
63.4 Kernel-Based Perceptron
63.5 Kernel-Based SVM
63.6 Kernel-Based Ridge Regression
63.7 Kernel-Based Learning
63.8 Kernel PCA
63.9 Inference under Gaussian Processes
63.10 Commentaries and Discussion
Problems
References
64 Generalization Theory
64.1 Curse of Dimensionality
64.2 Empirical Risk Minimization
64.3 Generalization Ability
64.4 VC Dimension
64.5 Bias–Variance Trade-off
64.6 Surrogate Risk Functions
64.7 Commentaries and Discussion
Problems
64.A VC Dimension for Linear Classifiers
64.B Sauer Lemma
64.C Vapnik–Chervonenkis Bound
64.D Rademacher Complexity
References
65 Feedforward Neural Networks
65.1 Activation Functions
65.2 Feedforward Networks
65.3 Regression and Classification
65.4 Calculation of Gradient Vectors
65.5 Backpropagation Algorithm
65.6 Dropout Strategy
65.7 Regularized Cross-Entropy Risk
65.8 Slowdown in Learning
65.9 Batch Normalization
65.10 Commentaries and Discussion
Problems
65.A Derivation of Batch Normalization Algorithm
References
66 Deep Belief Networks
66.1 Pre-Training Using Stacked Autoencoders
66.2 Restricted Boltzmann Machines
66.3 Contrastive Divergence
66.4 Pre-Training using Stacked RBMs
66.5 Deep Generative Model
66.6 Commentaries and Discussion
Problems
References
67 Convolutional Networks
67.1 Correlation Layers
67.2 Pooling
67.3 Full Network
67.4 Training Algorithm
67.5 Commentaries and Discussion
Problems
67.A Derivation of Training Algorithm
References
68 Generative Networks
68.1 Variational Autoencoders
68.2 Training Variational Autoencoders
68.3 Conditional Variational Autoencoders
68.4 Generative Adversarial Networks
68.5 Training of GANs
68.6 Conditional GANs
68.7 Commentaries and Discussion
Problems
References
69 Recurrent Networks
69.1 Recurrent Neural Networks
69.2 Backpropagation Through Time
69.3 Bidirectional Recurrent Networks
69.4 Vanishing and Exploding Gradients
69.5 Long Short-Term Memory Networks
69.6 Bidirectional LSTMs
69.7 Gated Recurrent Units
69.8 Commentaries and Discussion
Problems
References
70 Explainable Learning
70.1 Classifier Model
70.2 Sensitivity Analysis
70.3 Gradient X Input Analysis
70.4 Relevance Analysis
70.5 Commentaries and Discussion
Problems
References
71 Adversarial Attacks
71.1 Types of Attacks
71.2 Fast Gradient Sign Method
71.3 Jacobian Saliency Map Approach
71.4 DeepFool Technique
71.5 Black-Box Attacks
71.6 Defense Mechanisms
71.7 Commentaries and Discussion
Problems
References
72 Meta Learning
72.1 Network Model
72.2 Siamese Networks
72.3 Relation Networks
72.4 Exploration Models
72.5 Commentaries and Discussion
Problems
72.A Matching Networks
72.B Prototypical Networks

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