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Machine Learning : Algorithms and Applications.

Av: Medverkande: Materialtyp: TextDatum för upphovsrätt: ©2017Utgivningsuppgift: Milton : Taylor & Francis Group, 2016Utgåva: 1st edBeskrivning: 1 online resource (227 pages)Innehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9781315354415
Ämnen: Genre/form: DDK-klassifikation:
  • 006.31
Onlineresurser:
Innehåll:
Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Authors -- Introduction -- 1 Introduction to Machine Learning -- 1.1 Introduction -- 1.2 Preliminaries -- 1.2.1 Machine Learning: Where Several Disciplines Meet -- 1.2.2 Supervised Learning -- 1.2.3 Unsupervised Learning -- 1.2.4 Semi-Supervised Learning -- 1.2.5 Reinforcement Learning -- 1.2.6 Validation and Evaluation -- 1.3 Applications of Machine Learning Algorithms -- 1.3.1 Automatic Recognition of Handwritten Postal Codes -- 1.3.2 Computer-Aided Diagnosis -- 1.3.3 Computer Vision -- 1.3.3.1 Driverless Cars -- 1.3.3.2 Face Recognition and Security -- 1.3.4 Speech Recognition -- 1.3.5 Text Mining -- 1.3.5.1 Where Text and Image Data Can Be Used Together -- 1.4 The Present and the Future -- 1.4.1 Thinking Machines -- 1.4.2 Smart Machines -- 1.4.3 Deep Blue -- 1.4.4 IBM's Watson -- 1.4.5 Google Now -- 1.4.6 Apple's Siri -- 1.4.7 Microsoft's Cortana -- 1.5 Objective of This Book -- References -- SECTION I: SUPERVISED LEARNING ALGORITHMS -- 2 Decision Trees -- 2.1 Introduction -- 2.2 Entropy -- 2.2.1 Example -- 2.2.2 Understanding the Concept of Number of Bits -- 2.3 Attribute Selection Measure -- 2.3.1 Information Gain of ID3 -- 2.3.2 The Problem with Information Gain -- 2.4 Implementation in MATLAB[sup(®)] -- 2.4.1 Gain Ratio of C4.5 -- 2.4.2 Implementation in MATLAB -- References -- 3 Rule-Based Classifiers -- 3.1 Introduction to Rule-Based Classifiers -- 3.2 Sequential Covering Algorithm -- 3.3 Algorithm -- 3.4 Visualization -- 3.5 Ripper -- 3.5.1 Algorithm -- 3.5.2 Understanding Rule Growing Process -- 3.5.3 Information Gain -- 3.5.4 Pruning -- 3.5.5 Optimization -- References -- 4 Naïve Bayesian Classification -- 4.1 Introduction -- 4.2 Example -- 4.3 Prior Probability -- 4.4 Likelihood -- 4.5 Laplace Estimator -- 4.6 Posterior Probability.
4.7 MATLAB Implementation -- References -- 5 The k-Nearest Neighbors Classifiers -- 5.1 Introduction -- 5.2 Example -- 5.3 k-Nearest Neighbors in MATLAB[sup(®)] -- References -- 6 Neural Networks -- 6.1 Perceptron Neural Network -- 6.1.1 Perceptrons -- 6.2 MATLAB Implementation of the Perceptron Training and Testing Algorithms -- 6.3 Multilayer Perceptron Networks -- 6.4 The Backpropagation Algorithm -- 6.4.1 Weights Updates in Neural Networks -- 6.5 Neural Networks in MATLAB -- References -- 7 Linear Discriminant Analysis -- 7.1 Introduction -- 7.2 Example -- References -- 8 Support Vector Machine -- 8.1 Introduction -- 8.2 Definition of the Problem -- 8.2.1 Design of the SVM -- 8.2.2 The Case of Nonlinear Kernel -- 8.3 The SVM in MATLAB[sup(®)] -- References -- SECTION II: UNSUPERVISED LEARNING ALGORITHMS -- 9 k-Means Clustering -- 9.1 Introduction -- 9.2 Description of the Method -- 9.3 The k-Means Clustering Algorithm -- 9.4 The k-Means Clustering in MATLAB[sup(®)] -- 10 Gaussian Mixture Model -- 10.1 Introduction -- 10.2 Learning the Concept by Example -- References -- 11 Hidden Markov Model -- 11.1 Introduction -- 11.2 Example -- 11.3 MATLAB Code -- References -- 12 Principal Component Analysis -- 12.1 Introduction -- 12.2 Description of the Problem -- 12.3 The Idea behind the PCA -- 12.3.1 The SVD and Dimensionality Reduction -- 12.4 PCA Implementation -- 12.4.1 Number of Principal Components to Choose -- 12.4.2 Data Reconstruction Error -- 12.5 The Following MATLAB[sup(®)] Code Applies the PCA -- 12.6 Principal Component Methods in Weka -- 12.7 Example: Polymorphic Worms Detection Using PCA -- 12.7.1 Introduction -- 12.7.2 SEA, MKMP, and PCA -- 12.7.3 Overview and Motivation for Using String Matching -- 12.7.4 The KMP Algorithm -- 12.7.5 Proposed SEA -- 12.7.6 An MKMP Algorithm.
12.7.6.1 Testing the Quality of the Generated Signature for Polymorphic Worm A -- 12.7.7 A Modified Principal Component Analysis -- 12.7.7.1 Our Contributions in the PCA -- 12.7.7.2 Testing the Quality of Generated Signature for Polymorphic Worm A -- 12.7.7.3 Clustering Method for Different Types of Polymorphic Worms -- 12.7.8 Signature Generation Algorithms Pseudo-Codes -- 12.7.8.1 Signature Generation Process -- References -- Appendix I: Transcript of Conversations with Chatbot -- Appendix II: Creative Chatbot -- Index.
Sammanfattning: Machine learning, one of the top emerging sciences, has an extremely broad range of applications.
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Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Authors -- Introduction -- 1 Introduction to Machine Learning -- 1.1 Introduction -- 1.2 Preliminaries -- 1.2.1 Machine Learning: Where Several Disciplines Meet -- 1.2.2 Supervised Learning -- 1.2.3 Unsupervised Learning -- 1.2.4 Semi-Supervised Learning -- 1.2.5 Reinforcement Learning -- 1.2.6 Validation and Evaluation -- 1.3 Applications of Machine Learning Algorithms -- 1.3.1 Automatic Recognition of Handwritten Postal Codes -- 1.3.2 Computer-Aided Diagnosis -- 1.3.3 Computer Vision -- 1.3.3.1 Driverless Cars -- 1.3.3.2 Face Recognition and Security -- 1.3.4 Speech Recognition -- 1.3.5 Text Mining -- 1.3.5.1 Where Text and Image Data Can Be Used Together -- 1.4 The Present and the Future -- 1.4.1 Thinking Machines -- 1.4.2 Smart Machines -- 1.4.3 Deep Blue -- 1.4.4 IBM's Watson -- 1.4.5 Google Now -- 1.4.6 Apple's Siri -- 1.4.7 Microsoft's Cortana -- 1.5 Objective of This Book -- References -- SECTION I: SUPERVISED LEARNING ALGORITHMS -- 2 Decision Trees -- 2.1 Introduction -- 2.2 Entropy -- 2.2.1 Example -- 2.2.2 Understanding the Concept of Number of Bits -- 2.3 Attribute Selection Measure -- 2.3.1 Information Gain of ID3 -- 2.3.2 The Problem with Information Gain -- 2.4 Implementation in MATLAB[sup(®)] -- 2.4.1 Gain Ratio of C4.5 -- 2.4.2 Implementation in MATLAB -- References -- 3 Rule-Based Classifiers -- 3.1 Introduction to Rule-Based Classifiers -- 3.2 Sequential Covering Algorithm -- 3.3 Algorithm -- 3.4 Visualization -- 3.5 Ripper -- 3.5.1 Algorithm -- 3.5.2 Understanding Rule Growing Process -- 3.5.3 Information Gain -- 3.5.4 Pruning -- 3.5.5 Optimization -- References -- 4 Naïve Bayesian Classification -- 4.1 Introduction -- 4.2 Example -- 4.3 Prior Probability -- 4.4 Likelihood -- 4.5 Laplace Estimator -- 4.6 Posterior Probability.

4.7 MATLAB Implementation -- References -- 5 The k-Nearest Neighbors Classifiers -- 5.1 Introduction -- 5.2 Example -- 5.3 k-Nearest Neighbors in MATLAB[sup(®)] -- References -- 6 Neural Networks -- 6.1 Perceptron Neural Network -- 6.1.1 Perceptrons -- 6.2 MATLAB Implementation of the Perceptron Training and Testing Algorithms -- 6.3 Multilayer Perceptron Networks -- 6.4 The Backpropagation Algorithm -- 6.4.1 Weights Updates in Neural Networks -- 6.5 Neural Networks in MATLAB -- References -- 7 Linear Discriminant Analysis -- 7.1 Introduction -- 7.2 Example -- References -- 8 Support Vector Machine -- 8.1 Introduction -- 8.2 Definition of the Problem -- 8.2.1 Design of the SVM -- 8.2.2 The Case of Nonlinear Kernel -- 8.3 The SVM in MATLAB[sup(®)] -- References -- SECTION II: UNSUPERVISED LEARNING ALGORITHMS -- 9 k-Means Clustering -- 9.1 Introduction -- 9.2 Description of the Method -- 9.3 The k-Means Clustering Algorithm -- 9.4 The k-Means Clustering in MATLAB[sup(®)] -- 10 Gaussian Mixture Model -- 10.1 Introduction -- 10.2 Learning the Concept by Example -- References -- 11 Hidden Markov Model -- 11.1 Introduction -- 11.2 Example -- 11.3 MATLAB Code -- References -- 12 Principal Component Analysis -- 12.1 Introduction -- 12.2 Description of the Problem -- 12.3 The Idea behind the PCA -- 12.3.1 The SVD and Dimensionality Reduction -- 12.4 PCA Implementation -- 12.4.1 Number of Principal Components to Choose -- 12.4.2 Data Reconstruction Error -- 12.5 The Following MATLAB[sup(®)] Code Applies the PCA -- 12.6 Principal Component Methods in Weka -- 12.7 Example: Polymorphic Worms Detection Using PCA -- 12.7.1 Introduction -- 12.7.2 SEA, MKMP, and PCA -- 12.7.3 Overview and Motivation for Using String Matching -- 12.7.4 The KMP Algorithm -- 12.7.5 Proposed SEA -- 12.7.6 An MKMP Algorithm.

12.7.6.1 Testing the Quality of the Generated Signature for Polymorphic Worm A -- 12.7.7 A Modified Principal Component Analysis -- 12.7.7.1 Our Contributions in the PCA -- 12.7.7.2 Testing the Quality of Generated Signature for Polymorphic Worm A -- 12.7.7.3 Clustering Method for Different Types of Polymorphic Worms -- 12.7.8 Signature Generation Algorithms Pseudo-Codes -- 12.7.8.1 Signature Generation Process -- References -- Appendix I: Transcript of Conversations with Chatbot -- Appendix II: Creative Chatbot -- Index.

Machine learning, one of the top emerging sciences, has an extremely broad range of applications.

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