Machine learning algorithms and applications
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| Format: | Buch |
| Sprache: | English |
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Boca Raton
CRC Press
2017
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| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV044294868 | ||
| 003 | DE-604 | ||
| 005 | 20230403 | ||
| 007 | t| | ||
| 008 | 170503s2017 xxu |||| 00||| eng d | ||
| 020 | |a 9781498705387 |9 978-1-4987-0538-7 | ||
| 035 | |a (OCoLC)992474403 | ||
| 035 | |a (DE-599)BVBBV044294868 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c US | ||
| 049 | |a DE-739 |a DE-573 | ||
| 050 | 0 | |a Q325.5 | |
| 082 | 0 | |a 006.3/12 |2 23 | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 100 | 1 | |a Mohammed, Mohssen |d 1982- |0 (DE-588)1036606244 |4 aut | |
| 245 | 1 | 0 | |a Machine learning |b algorithms and applications |c Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier |
| 264 | 1 | |a Boca Raton |b CRC Press |c 2017 | |
| 300 | |a XXII, 204 Seiten | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Khan, Muhammad Badruddin |e Sonstige |4 oth | |
| 700 | 1 | |a Bashier, Eihab Bashier Mohammed |e Sonstige |0 (DE-588)1122443072 |4 oth | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029698898&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-029698898 | |
Datensatz im Suchindex
| _version_ | 1819763317188591616 |
|---|---|
| adam_text | Contents
Preface...............................................xiii
Acknowledgments.........................................xv
Authors...............................................xvii
Introduction...........................................xix
1 Introduction to Machine Learning....................1
1.1 Introduction....................................1
1.2 Preliminaries...................................2
1.2.1 Machine Learning: Where Several
Disciplines Meet..........................4
1.2.2 Supervised Learning......................7
1.2.3 Unsupervised Learning....................9
1.2.4 Semi-Supervised Learning................10
1.2.5 Reinforcement Learning..................11
1.2.6 Validation and Evaluation...............11
1.3 Applications of Machine Learning Algorithms....14
1.3.1 Automatic Recognition of Handwritten
Postal Codes.............................15
1.3- 2 Computer-Aided Diagnosis................17
1.3- 3 Computer Vision.........................19
1.3·3-1 Driverless Cars..................20
1.3.3-2 Face Recognition and Security....22
1.3.4 Speech Recognition......................22
-Ï
vii
viii ■ Contents
1.3- 5 Text Mining.............................23
1.3.5.1 Where Text and Image Data Can
Be Used Together..................24
1.4 The Present and the Future.....................25
1.4.1 Thinking Machines.......................25
1.4.2 Smart Machines..........................28
1.4.3 Deep Blue...............................30
1.4.4 IBM’s Watson............................31
1.4.5 Google Now..............................32
1.4.6 Apple’s Siri............................32
1.4.7 Microsoft’s Cortana.....................32
1.5 Objective of This Book..........................33
References..........................................34
SECTION I SUPERVISED LEARNING ALGORITHMS
2 Decision Trees.....................................3 1
2.1 Introduction....................................37
2.2 Entropy.........................................38
2.2.1 Example.................................38
2.2.2 Understanding the Concept of Number
of Bits..................................40
2.3 Attribute Selection Measure.....................41
2.3- 1 Information Gain of ID3.................41
2.3- 2 The Problem with Information Gain.......44
2.4 Implementation in MATLAB®.......................46
2.4.1 Gain Ratio of C4.5........49
2.4.2 Implementation in MATLAB................51
References..........................................52
3 Rule-Based Classifiers..............................53
3.1 Introduction to Rule-Based Classifiers..........53
3-2 Sequential Covering Algorithm...................54
3-3 Algorithm.......................................54
3-4 Visualization...................................55
3.5 Ripper..........................................55
3-5.1 Algorithm................................56
Contents ■ ¡X
3.5.2 Understanding Rule Growing Process.......58
3.5.3 Information Gain..........................65
3.5.4 Pruning...................................66
3.5.5 Optimization..............................68
References...........................................72
4 Naïve Bayesian Classification........................73
4.1 Introduction...................................73
4.2 Example........................................74
4.3 Prior Probability..............................75
4.4 Likelihood.....................................75
4.5 Laplace Estimator..............................77
4.6 Posterior Probability..........................78
4.7 MATLAB Implementation..........................79
References...........................................82
5 The fe-Nearest Neighbors Classifiers...............83
5 · 1 Introduction...................................83
5.2 Example........................................84
5.3 ^-Nearest Neighbors in MATLAB®.................86
References...........................................88
6 Neural Networks......................................89
6.1 Perceptron Neural Network......................89
6.1.1 Perceptrons...............................90
6.2 MATLAB Implementation of the Perceptron
Training and Testing Algorithms.................94
6.3 Multilayer Perceptron Networks.................96
6.4 The Backpropagation Algorithm..................99
6.4.1 Weights Updates in Neural Networks.......101
6.5 Neural Networks in MATLAB.....................102
References..........................................105
7 Linear Discriminant Analysis........................107
7.1 Introduction..................................107
7.2 Example.......................................108
References..........................................114
x ■ Contents
8 Support Vector Machine............................115
8.1 Introduction................................115
8.2 Definition of the Problem...................116
8.2.1 Design of the SVM....................120
8.2.2 The Case of Nonlinear Kernel.........126
8.3 The SVM in MATLAB®..........................127
References........................................128
SECTION II UNSUPERVISED LEARNING ALGORITHMS
9 fe-Means Clustering...............................131
9.1 Introduction................................131
9.2 Description of the Method...................132
9-3 The ¿-Means Clustering Algorithm............133
9.4 The ¿֊Means Clustering in MATLAB®...........134
10 Gaussian Mixture Model............................137
10.1 Introduction................................137
10.2 Learning the Concept by Example.............138
References........................................143
11 Hidden Markov Model...............................145
11.1 Introduction................................145
11.2 Example.....................................146
11.3 MATLAB Code.................................148
References........................................152
12 Principal Component Analysis......................153
12.1 Introduction................................153
12.2 Description of the Problem..................154
12.3 The Idea behind the PCA.....................155
12.3.1 The SVD and Dimensionality
Reduction.............................157
12.4 PCA Implementation..........................158
12.4.1 Number of Principal Components
to Choose.............................159
12.4.2 Data Reconstruction Error............160
4
Contents ■ xi
12.5 The Following MATLAB® Code Applies
the PC A.....................................161
12.6 Principal Component Methods in Weka..........163
12.7 Example: Polymorphie Worms Detection
Using PCA....................................167
12.7.1 Introduction..........................167
12.7.2 SEA, MKMP, and PCA....................168
12.7.3 Overview and Motivation for Using
String Matching.......................l69
12.7.4 The KMP Algorithm.....................170
12.7.5 Proposed SEA..........................171
12.7.6 An MKMP Algorithm.....................173
12.7.6.1 Testing the Quality of the
Generated Signature for
Polymorphic Worm A............174
12.7.7 A Modified Principal Component
Analysis..............................174
12.7.7.1 Our Contributions in the PCA.174
12.7.7.2 Testing the Quality of
Generated Signature for
Polymorphic Worm A............178
12.7.7.3 Clustering Method for Different
Types of Polymorphic Worms....179
12.7.8 Signature Generation Algorithms
Pseudo-Codes..........................179
12.7.8.1 Signature Generation Process.180
References.........................................187
Appendix I: Transcript of Conversations
with Chatbot...............................189
Appendix II: Creative Chatbot..........................193
Index..................................................195
|
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| id | DE-604.BV044294868 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-24T05:56:27Z |
| institution | BVB |
| isbn | 9781498705387 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029698898 |
| oclc_num | 992474403 |
| open_access_boolean | |
| owner | DE-739 DE-573 |
| owner_facet | DE-739 DE-573 |
| physical | XXII, 204 Seiten |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | CRC Press |
| record_format | marc |
| spellingShingle | Mohammed, Mohssen 1982- Machine learning algorithms and applications Machine learning Computer algorithms Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning algorithms and applications |
| title_auth | Machine learning algorithms and applications |
| title_exact_search | Machine learning algorithms and applications |
| title_full | Machine learning algorithms and applications Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier |
| title_fullStr | Machine learning algorithms and applications Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier |
| title_full_unstemmed | Machine learning algorithms and applications Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier |
| title_short | Machine learning |
| title_sort | machine learning algorithms and applications |
| title_sub | algorithms and applications |
| topic | Machine learning Computer algorithms Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Computer algorithms Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029698898&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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