Machine learning and big data concepts, algorithms, tools and applications
"Machine learning with big data technologies create new opportunities to understand the various data process related to medical or environmental aspects of agriculture. Machine learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland sec...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley-Scrivener
2020
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| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Thaventhiran 6.1 Classifier 132 6.1.1 Introduction 132 6.1.2 Explanation-Based Learning 133 6.1.3 Isomorphism and Clique Method 135 6.1.4 Context-Dependent Classification 138 6.1.5 Summary 139 6.2 Feature Processing 140 6.2.1 Introduction 140 6.2.2 Detection and Extracting Edge With Boundary Line 141 6.2.3 Analyzing the Texture 142 6.2.4 Feature Mapping in Consecutive Moving Frame 143 6.2.5 Summary 145 6.3 Clustering 145 6.3.1 Introduction 145 6.3.2 Types of Clustering Algorithms 146 6.3.2.1 Dynamic Clustering Method 148 6.3.2.2 Model-Based Clustering 148 6.3.3 Application 149 6.3.4 Summary 150 6.4 Conclusion 151 References 151 Section 3: Machine Learning: Algorithms & Applications 153 7 Machine Learning 155; Elham Ghanbari and Sara Najafzadeh 7.1 History and Purpose of Machine Learning
- One Variable Gaussian) 50 2.6.1.2 Degenerate Univariate Gaussian 51 2.6.1.3 Multivariate Gaussian 51 References 51 3 Correlation and Regression 53; Mohd. Abdul Haleem Rizwan 3.1 Introduction 53 3.2 Correlation 54 3.2.1 Positive Correlation and Negative Correlation 54 3.2.2 Simple Correlation and Multiple Correlation 54 3.2.3 Partial Correlation and Total Correlation 54 3.2.4 Correlation Coefficient 55 3.3 Regression 57 3.3.1 Linear Regression 64 3.3.2 Logistic Regression 64 3.3.3 Polynomial Regression 65 3.3.4 Stepwise Regression 66 3.3.5 Ridge Regression 67 3.3.6 Lasso Regression 67 3.3.7 Elastic Net Regression 68 3.4 Conclusion 68 References 69 Section 2: Big Data and Pattern Recognition 71 4 Data Preprocess 73; Md
- Includes bibliographical references and index
- And Eigendecomposition of a Matrix 15 1.2.1 Characteristics Polynomial 16 1.2.1.1 Some Results on Eigenvalue 16 1.2.2 Eigendecomposition 18 1.3 Introduction to Calculus 20 1.3.1 Function 20 1.3.2 Limits of Functions 21 1.3.2.1 Some Properties of Limits 22 1.3.2.2 1nfinite Limits 25 1.3.2.3 Limits at Infinity 26 1.3.3 Continuous Functions and Discontinuous Functions 26 1.3.3.1 Discontinuous Functions 27 1.3.3.2 Properties of Continuous Function 27 1.3.4 Differentiation 28 References 29 2 Theory of Probability 31; Parvaze Ahmad Dar and Afroz 2.1 Introduction 31 2.1.1 Definition 31 2.1.1.1 Statistical Definition of Probability 31 2.1.1.2 Mathematical Definition of Probability 32 2.1.2 Some Basic Terms of Probability 32 2.1.2.1 Trial and Event 32 2.1.2.2 Exhaustive Events (Exhaustive Cases) 33 2.1.2.3 Mutually
- Preface xix Section 1: Theoretical Fundamentals 1 1 Mathematical Foundation 3; Afroz and Basharat Hussain 1.1 Concept of Linear Algebra 3 1.1.1 Introduction 3 1.1.2 Vector Spaces 5 1.1.3 Linear Combination 6 1.1.4 Linearly Dependent and Independent Vectors 7 1.1.5 Linear Span, Basis and Subspace 8 1.1.6 Linear Transformation (or Linear Map) 9 1.1.7 Matrix Representation of Linear Transformation 10 1.1.8 Range and Null Space of Linear Transformation 13 1.1.9 Invertible Linear Transformation 15 1.2 Eigenvalues, Eigenvectors
- Of Things 121 5.6.11 Weather Forecasting 121 5.7 Where IoT Meets Big Data 122 5.7.1 IoT Platform 122 5.7.2 Sensors or Devices 123 5.7.3 Device Aggregators 123 5.7.4 IoT Gateway 123 5.7.5 Big Data Platform and Tools 124 5.8 Role of Machine Learning For Big Data and IoT 124 5.8.1 Typical Machine Learning Use Cases 125 5.9 Conclusion 126 References 127 6 Pattern Recognition Concepts 131; Ambeshwar Kumar, R. Manikandan and C.