2.8 Illustration of the nearest neighbor (NN) classification algorith...Figure 2.9 Illustration of the plane partitioning of a two‐dimensional datas...Figure 2.10 Illustration of the nearest neighbor (NN) decision boundary.Figure 2.11 Example of clustering.Figure 2.12 k‐Means algorithm.Figure 2.13 k = 3 means clustering on 2D dataset.Figure 2.14 Concept of data projection.Figure 2.15 Successive data projections.Figure 2.16 Decision tree presenting response to direct mailing.Figure 2.17 Predicting email spam.Figure 2.18 Top‐down algorithmic framework for decision tree induction. The ...Figure 2.19 Black circles represent the input data, xn; red squares repres...2 Chapter 3Figure 3.1 From biological to mathematical simplified model of a neuron.Figure 3.2 Block diagram of feedforward network.Figure 3.3 Schematic representation of supervised learning.Figure 3.4 Illustration of backpropagation.Figure 3.5 Finite impulse response (FIR) neuron and neural network.Figure 3.6 Finite impulse response (FIR) network unfolding.Figure 3.7 Temporal backpropagation.Figure 3.8 Oversimplified finite impulse response (FIR) network.Figure 3.9 Network prediction configuration.Figure 3.10 Nonlinear AR/ARMA predictors.Figure 3.11 Recurrent neural network.Figure 3.12 Canonical form of a recurrent neural network for prediction.Figure 3.13 Recurrent neural network (RNN) architectures: (a) activation fee...Figure 3.14 General locally recurrent–globally feedforward (LRGF) architectu...Figure 3.15 An example of Elman recurrent neural network (RNN).Figure 3.16 An example of Jordan recurrent neural network (RNN).Figure 3.17 A fully connected recurrent neural network (RNN; Williams–Zipser...Figure 3.18 Nonlinear IIR filter structures. (a) A recurrent nonlinear neura...Figure 3.19 A long short‐term memory (LSTM) memory cell.Figure 3.20 A bidirectional recurrent neural network (BRNN). (for more detai...Figure 3.21 (Top) Cellular neural networks (CeNN) architecture, (bottom) cir...Figure 3.22 Memristor‐based cellular nonlinear/neural network (MCeNN).Figure 3.23 Illustration of the convolution operation. If we overlap the con...Figure 3.24 RGB image/three channels and three kernels. (for more details se...Figure 3.25 Computing∂z/∂X. (for more details see the color fig...Figure 3.26 Illustration of pooling layer operation. (for more details see t...Figure 3.27 Illustration of preprocessing in a cooperative neural network (C...
3 Chapter 4Figure 4.1 (a) Neural network (NN) as a classifier, (b) NN during the releva...Figure 4.2 Relevance propagation.Figure 4.3 Relevance propagation (heat map; relevance is presented by the in...Figure 4.4 Example of membership functions versus the traffic volume and net...Figure 4.5 A schematic representation of the Mamdani inference algorithm.Figure 4.6 Illustration of the soft margin for a linear support vector machi...
4 Chapter 5Figure 5.1 Operation of GraphSAGE: (a) sample neighborhood, (b) aggregate fe...Figure 5.2 Illustrations of ConvGNN network: (a) A ConvGNN with multiple gra...Figure 5.3 2D Convolution versus graph convolution: (a) 2D convolution. Anal...Figure 5.4 Parametric graph convolution: (a) Conventional graph convolutiona...Figure 5.5 A ConvGNN with pooling and readout layers for graph classificatio...Figure 5.6 A graph autoencoder (GAE) for network embedding. The encoder uses...Figure 5.7 A STGNN for spatial‐temporal graph forecasting. A graph convoluti...Figure 5.8 A subset of the Web.Figure 5.9 Graph and the neighborhood of a node. The state x1 of node 1 depe...Figure 5.10 Graph (on the top, left), the corresponding encoding network (to...Figure 5.B.1 A directed graph and the corresponding matrices.
5 Chapter 6Figure 6.1 Routing game with two populations of players. Source: Krichene et...Figure 6.2 Examples of different types of stage games.Figure 6.3 In brackets: original version of Pigou’s network. no brackets: no...Figure 6.4 Braess’s paradox. (a) Initial network, (b) augmented network. The...Figure 6.5 (a) In atomic instances with affine cost functions, different equ...Figure 6.6 (Left) Example of embedding problems with zero‐cost cycles. Wavy ...
6 Chapter 7Figure 7.1 Algorithm for autonomous channel and power level selection.Figure 7.2 Major use cases of each self‐organizing network (SON) function....Figure 7.3 Transfer learning (TL)‐based network state information updating....Figure 7.4 Transition kernel: Markov decision process model of the system dy...Figure 7.5 Simulation scenario: network and traffic parameters.Figure 7.6 Time‐averaged parameters.Figure 7.7 Cell cluster example.Figure 7.8 Popularity
