A

Activation functions, 92f, 99

Agents, 11

Alice’s Adventures in Wonderland (Novel), 9

Alignment problem, 145

Antecedent, rule, 53

Apple’s Siri, 32

Artificial neural network (ANN), 90

Attribute selection measure, 41–46

information gain of ID3, 41–44

problem with information gain, 44–46

Automatic recognition of handwritten postal codes, 15–17

Autonomous cars, 20–21, 22f

Autonomous robots, 20

Axon, 89

B

Backpropagation algorithm, 99–102

stages, 99

weights updates in neural network, 101–102

Bayes’ theorem

equation, 73

posterior probability, 74

Belief, stability of (Plato), 12–13

Bias, perceptron, 92

Biometric verification, GMM, 138

Bits, concept of, 40–41

Brant, Kenneth F., 28

Building stage, JRip algorithm, 57

Business intelligence system, 23

C

C4.5 (Successor of ID3), 38

attribute selection measure, 41

gain ratio of, 49–51

Cars, autonomous, 20–21, 22f

Cellan-Jones, Rory, 26

transcript of conversation with Chatbot Rose, 189–192, 190f

Chatbot

creative, 193–194

transcript of conversations with, 189–192, 190f

Turing test, 25–26

conversation with human and machine, 26–27

by Eugene Goostman, 26

neural conversational model, 28

Class conditional independence, 74

Classifiers, 24–25

k-NN, 131–132

algorithm, 83

classification, 83–84

example, 84–86, 85f

in MATLAB^®, 86–88

regression, 83–84

shortcoming of, 84

naïve Bayesian, 73

example, 74–75

Laplace estimator, 77–78

likelihood, 75–76

MATLAB implementation, 79–82

posterior probability, 78–79

prior probability, 75

rule-based, 53

algorithm, 54–55

IF-THEN rules, 53

overview, 53

Ripper, 55–72

sequential covering algorithm, 54, 56f

visualization, 55

Clustering

algorithm, 116, 154

k-means

algorithm, 133–134

in MATLAB^®, 134–136

method description, 132–133

overview, 131–132

method for polymorphic worms, 179

support vector, 116

using GMM, 138–142, 142f

Common weighing scheme, 84

Compute Array of Frequencies function, 183–184

Compute Principal Component function, 183, 185

Computer-aided diagnosis, 17–19

assisting doctors/radiologists in health problems, 19

classifier, examples, 18

pattern recognition, 17–18

Computers, 3

Computer vision, 19–22

driverless cars, 20–21, 22f

face recognition and security, 22

RoboCup, 19–20, 20f

Conditional probabilities

after Laplace correction, 77

of likelihood, 76

Consequent, rule, 53

Continuous space K, 85

Cortana, Microsoft’s, 32–33

Covariance matrix, 154–155, 157

benefits into SVD, 158

calculation, 110–111

ecoli dataset, 155t

evaluation, 177

“cov()” command of MATLAB, 110

Creative chatbot, 193–194

Cross-validation technique, 14

D

Dataset/data

2D random, 155, 156f

2D reduced, 163f

classification in DT, 37

cross-validation, 14

ecoli

covariance matrix of, 155t

k-means clustering, 134, 135f

k-NN classifier, 86–87

neural network model, 102–105

variance of principle component, 160, 160t

entropy of, 39, 39t, 43

function “infogain” to calculate entropy of, 47–48

for growing rule, 67, 69

in optimization stage, 71

information gain, 65

labeled

semi-supervised learning, 11

supervised learning, 8

linearly separable, 116, 117f

machine learning, 7f

mining, 4

MPCA contributions in polymorphic worms detection

mean adjusted data, 176–177

normalization of data, 176

projection of data, adjusting, 178

significant data determination, 175–176

naïve Bayesian classifier, 74–75

nonlinearly separable, 126f

for pruning grown rules, 67, 69

in optimization stage, 71

reconstruction error, 160–161, 161t

rule growing, 58–59, 64

text mining, 24–25

unlabeled

semi-supervised learning, 10

supervised learning, 8, 9t

Decision tree (DT), 37

attribute selection measure, 41–46

information gain of ID3, 41–44

problem with information gain, 44–46

data classification, 37

entropy, 38–41

concept of number of bits, 40–41

example, 38–39, 39t

function “infogain,” 47–48

Shannon, 38

MATLAB^®, implementation in, 46–52

Deep Blue (IBM), 30–31

Deep Fritz (chess program), 31

Dendrites, 89

Dimensionality reduction, SVD and, 157–158

Discrete and finite space K, 85

Double-honeynet system, 167–168, 171

Driverless cars, Toyota, 20–21, 22f

Durant, Will, 1

D-variate Gaussian function, 137

E

Ecoli data

covariance matrix of, 155t

k-means clustering, 134, 135f

k-NN classifier, 86–87

neural network model, 102–105

variance of principle component, 160, 160t

Eigenvalue evaluation, 177

Entropy, 38–41

concept of number of bits, 40–41

example, 38–39, 39t

function “infogain,” 47–48

Shannon, 38

Epoch, 94

Eugene Goostman (chatbot), 25

Expectation maximization (EM) algorithm, iterative, 138

Expected value of information, 38

F

Feature descriptor (FD), 178

Feed-forward stage, 99

Finite space K, 85

Fisher, Ronald, 107

Function “gaininfo,” 51–52

Function “infogain,” 52

to calculate entropy of dataset, 47–48

G

Gain ratio, 46, 49

of C4.5, 49–51

Gartner, Inc., 28–29

Gates, Bill, 14

Gaussian kernel, 126

Gaussian mixture model (GMM), 137–142

applications, 138

clustering using, 138

concept by example, 138–142

equation, 137

Gaussian distributions

clusters corresponding to, 142f

means and variances of, 139t, 140f

mixed, 141f

Google, 17

Google Now, 32

Gradient decent method, 101

Grow phase, JRip algorithm, 57

H

Handwriting detection, 24

Handwritten postal codes, automatic recognition of, 15–17

Hidden Markov model (HMM), 17

example, 146–147

MATLAB code, 148–152

overview, 145–146

parameters, 148f

problems of, 145

Hold out testing/validation, 14

Huffman code, 40

Human and machine, conversation with, 26–27

I

IBM’s Deep Blue, 30–31

IBM’s Watson, 31–32

ID3 (Iterative Dichotomiser 3), 38

information gain of, 41–44

Successor of ID3 (C4.5), 38

attribute selection measure, 41

gain ratio of, 49–51

IDS, see Intrusion detection system (IDS)

IF-THEN rules, rule-based classifiers, 53

Information gain

formula for, 66

measure

drawback, 46

formula of, 42

of ID3, 41–44

problem with, 44–46

splitinfo, 41

Ripper, 65–66

rule growing process using, 60–65

Instance-based learning, 84

Internet worms, 167

Intrusion detection system (IDS), 169

Iris database, 127

Irwin, Terence, 12

Iterative expectation maximization (EM) algorithm, 138

J

JRip algorithm, 56–58

building stage, 57

optimization stage, 57–58

K

Kasparov, Garry, 30

Kernel

case of nonlinear, 126–127

Gaussian, 126

linear function, 125, 127

nonlinear function, 126–127

trick, 115

k-fold cross-validation, 14

k-means clustering

algorithm, 133–134

in MATLAB^®, 134–136

method description, 132–133

overview, 131–132

KMP, see Knuth–Morris–Pratt (KMP) algorithm

Kmpfound function, 181–182

k-nearest neighbors (k-NN) classifiers, 131–132

algorithm, 83

classification, 83–84

example, 84–86, 85f

in MATLAB^®, 86–88

regression, 83–84

shortcoming of, 84

Knowledge Discovery from Data (KDD), 4

Knuth–Morris–Pratt (KMP) algorithm, 168, 170–171, 173

Kramnik, Vladimir, 31

Kuhn–Tucker theorem, 123

L

Labeled data

semi-supervised learning, 11

supervised learning, 8

Lagrangian function, 121–123

Laplace estimator, 77–78

Lazy learning, 84

Learning, see specific learning

Learning rate, 94, 101

Likelihood probability, 73, 75–76, 78

code, 80

conditional probabilities, 76

Linear discriminant analysis (LDA), 107–113

example, 108–113

overview, 107

Linear kernel function, 125, 127

Linearly separable data, 116, 117f

Loebner Prize, 189

Luhn, H.P., 23

M

Machine(s)

conversation with human and, 26–27

smart, 3, 28–30

criteria for, 28–29

prediction (2014 and 2015), 30

strategic technologies, 29, 29f

support vector machines (SVM)

in MATLAB^®, 127–128

overview, 115–116

problem definition, 116–119

Machine learning algorithms, 4–7, 37, 115

applications of, 14–25

automatic recognition of handwritten postal codes, 15–17

computer-aided diagnosis, 17–19

computer vision, 19–22

speech recognition, 22–23

text mining, 23–25

discipline of, 5–6, 5f

goal of, 6

present and future, 25–33

Apple’s Siri, 32

Deep Blue (IBM), 30–31

Google Now, 32

IBM’s Watson, 31–32

Microsoft’s Cortana, 32–33

smart machines, 28–30

thinking machines, 25, 27–28

techniques and required data, 7f

Margin, SVM, 116, 118f

MATLAB^®

code

applies PCA, 161–162

covariance matrix calculation, 110–111

GMM, 138–142

hidden Markov model, 148–152

implementation, 46–52

gain ratio of C4.5, 49–51

naïve Bayesian classification, 79–82

perceptron training and testing algorithms, 94–96

prediction process in, 81–82

k-means clustering, 134–136

k-NN algorithm in, 86–88

neural networks in, 102–105

SVM in, 127–128

McCulloch, Warren, 90

Mean adjusted data, 176–177

Means of Gaussian distributions, 139t, 140f

Microsoft’s Cortana, 32–33

Minimum description length (MDL), 57, 70, 72

Mitchell, Tom, 5–6

Modified Knuth–Morris–Pratt (MKMP) algorithm, 170, 172–174

SEA and PCA, 168

testing quality of generated signature, 174

Modified PCA (MPCA), polymorphic worms detection, 174–179

clustering method for worms, 179

contributions in, 174–178

covariance matrix evaluation, 177

eigenvalue evaluation, 177

frequency counts determination, 175

mean adjusted data, 176–177

normalization of data, 176

principal component evaluation, 177–178

projection of data, adjusting, 178

significant data determination, 175–176

quality testing of generated signature, 178–179

Multilayer perceptron network, 96–99

N

Naïve Bayesian classification, 73

example, 74–75

Laplace estimator, 77–78

likelihood, 75–76

MATLAB implementation, 79–82

posterior probability, 78–79

prior probability, 75

Nearest centroid classifier, see Rocchio algorithm

Neural conversational model, 28

Neural network

error histogram, 104f

in MATLAB, 102–105

multilayer, 96–99

perceptron, 89–94

validation performance, 104f

weights updates in, 101–102

Neuron, 89, 90f

Nonlinear kernel function, 126–127

Nonlinearly separable data, 126f

O

Optical character recognition (OCR) technology, 15–17, 16f

Optimization, Ripper, 68–72

Optimization stage

dataset for growing rule, 71

dataset for pruning rules, 71

JRip algorithm, 57–58

Overall variability, PCA, 154

Overfitting phenomenon, 13–14

P

Pattern (string), 169

Pattern recognition, 4

computer-aided diagnosis, 17–18

HMM, 145

k-NN algorithm, 83

OCR technology, 17

PCA, see Principal component analysis (PCA)

Perceptron

neural network, 89–94

training and testing algorithm, MATLAB implementation, 94–96

PerceptronTesting function, 95–96

Pitts, Walter, 90

Plato on stability of belief, 12–13

Plato’s Ethics (Book), 12

The Pleasures of Philosophy (Book), 1

Polymorphic worms detection using PCA, 167–187

KMP algorithm, 170–171

MKMP algorithm, 173–174

modified PCA, 174–179

clustering method for worms, 179

contributions in, 174–178

quality testing of generated signature, 178–179

overview, 167–168

proposed SEA, 171–172, 172f, 172t

SEA, MKMP, and PCA, 168

signature generation algorithms pseudo-codes, 179–187

MKMP algorithm pseudo-code, 181–183

MPCA pseudo-code, 183–186

quality testing of generated signature, 186–187

SEA pseudo-code, 180

string matching, 169–170

testing quality of generated signature, 174

Posterior probability, 73, 78–79

formula, 78

requirements, 78

using Bayes’ theorem, 74

Preliminaries, 2–14

machine learning, 4–7

reinforcement learning, 11

semi-supervised learning, 10–11

labeled data, 11

unlabeled data, 10

supervised learning, 7–8

categories, 8

labeled data, 8

unlabeled data, 8, 9t

unsupervised learning, 9–10

validation and evaluation, 11–14

Principal component, 153–154

evaluation, 177–178

methods in Weka, 163–167

projection of data, adjusting, 178

Principal component analysis (PCA)

2D reduced data, 163f

3D reduced space, 163f

defined, 153–154

idea behind, 155–158

dataset shape, 156f

SVD and dimensionality reduction, 157–158

implementation, 158–161

data reconstruction error, 160–161, 161t

principle components selection, 159–160, 159t

steps, 158–159

MATLAB^®, 161–162

methods in Weka, 163–167

polymorphic worms detection using, 167–187

KMP algorithm, 170–171

MKMP algorithm, 173–174

modified PCA, 174–179

overview, 167–168

proposed SEA, 171–172, 172f, 172t

SEA, MKMP, and PCA, 168

signature generation algorithms pseudo-codes, 179–187

string matching, 169–170

testing quality of generated signature, 174

problem description, 154–155

purpose of using, 157

Prior probability, 73, 75

of class variable, 79–80

Laplace estimator, 77

Prune phase, JRip algorithm, 57

Pruning metric, 68

Pruning operation, 56, 66–68

Pseudo-codes, signature generation algorithms, 179–187

MKMP algorithm pseudo-code, 181–183

MPCA pseudo-code, 183–186

quality testing of generated signature, 186–187

SEA pseudo-code, 180

Q

Quadratic programming (QP) problem, 121–122

Quality testing of generated signature, 168

MKMP algorithm, 174

MPCA, 178–179

pseudo-codes for, 186–187

R

Radial basis functions (RBFs) kernels, 126, 127f

Reconstruction error, 160–161, 161t

Reduced error pruning (REP), 55

Regression, k-NN, 83–84

Reinforcement learning, 11

Replacement rule, 70

Revised rule, 70

Ripper (repeated incremental pruning to produce error reduction), 55–72

algorithm of JRip, 56–58

building stage, 57

optimization stage, 57–58

information gain, 65–66

optimization, 68–72

pruning, 66–68

rule growing process, 58–65

RoboCup (Robot Soccer World Cup), 19–20, 20f

Robots, autonomous, 20

Rocchio algorithm, 132

Romeo and Juliet (Play), 3

Rosenblatt, Frank, 90

Rose (Chatbot), transcript of conversation with, 189–192, 190f

Rule antecedent, 53

Rule-based classifiers, 53

algorithm, 54–55

IF-THEN rules, 53

overview, 53

Ripper, 55–72

algorithm of JRip, 56–58

information gain, 65–66

optimization, 68–72

pruning, 66–68

rule growing process, 58–65

sequential covering algorithm, 54, 56f

visualization, 55

Rule consequent, 53

Rule growing process, 58–65

attributes and possibilities, 60

benchmark dataset, 58–59

using information gain, 60–65

S

SAS Institute Inc., 4

Scoring problem, 145

SEA, see Substring exaction algorithm (SEA)

Semi-supervised learning, 10–11

labeled data, 11

unlabeled data, 10

Sequential covering algorithm, 54, 56f

Shannon entropy, 38

Sigmoid function, 92, 100

Signaturefile function, 181–183

Signature generation algorithms pseudo-codes, 179–187

MKMP algorithm pseudo-code, 181–183

MPCA pseudo-code, 183–186

quality testing of generated signature, 186–187

SEA pseudo-code, 180

Sign function, 91

Singular value decomposition (SVD), 157–158

benefits of covariance matrix into, 158

and dimensionality reduction, 157–158

Siri (speech interpretation and recognition interface), Apple’s, 32

Smart machines, 3, 28–30

criteria for, 28–29

prediction (2014 and 2015), 30

strategic technologies, 29, 29f

Social media, 23

Soma, 89

Speaker identification, GMM, 138

Speech recognition, 22–23

Splitinfo (measure of information gain), 41, 49–52

Statistical Analysis System (SAS), 4

Step function, 91

String matching, 169–170

Substring exaction algorithm (SEA), 168, 170

proposed, 171–173

pseudo-code, 180

Successor of ID3 (C4.5), 38

attribute selection measure, 41

gain ratio of, 49–51

Supervised learning, 7–8; see also Unsupervised learning

algorithms

DT, 37–52

k-NN classifiers, 83–88

LDA, 107–113

naïve Bayesian classification, 73–82

neural networks, 88–105

rule-based classifiers, 53–72

SVM, 115–128

categories, 8

labeled data, 8

unlabeled data, 8, 9t

Support vector clustering, 116

Support vector machines (SVM)

in MATLAB^®, 127–128

overview, 115–116

problem definition, 116–119

case of nonlinear kernel, 126

design, 120–126

Support vectors, 119, 119f, 121

SVD, see Singular value decomposition (SVD)

Svmtrain function, 127

T

TestingData, 127

Text (string), 169

Text mining, 23–25

applications, 23–24

text and image data, 24–25

Three-dimensional (3D) hyperplane, 116, 117f

Three-dimensional reduced space, 163f

Top-down induction of decision trees (TDIDTs), 37

Toyota, driverless cars, 20–21, 22f

Training problem, HMM, 145

Transcript of conversation with Chatbot Rose, 189–192, 190f

Turing, Alan, 3, 25

Two-dimensional (2D) random data, 155, 156f

Two-dimensional reduced data, 163f

U

Unlabeled data

semi-supervised learning, 10

supervised learning, 8, 9t

Unsupervised learning, 9–10; see also Supervised learning

algorithms

GMM, 137–142

HMM, 145–152

k-means clustering, 131–136

PCA, 153–187

clustering, 10, 115–116

US Postal Service, OCR technology, 15–17, 16f

V

Variances of Gaussian distributions, 139t, 140f

Visualization, rule-based classifiers, 55

Voice-controlled programs, 22–23

W

Watson, Thomas J., 31

Weka

principal component methods in, 163–167

Ripper in, 56–58

Writing style detection, 24

X

XOR logical operation, 96–97, 97f, 97t