Handbook of Machine Learning for Computational Optimization

Applications and Case Studies

Vishal Jain, Sapna Juneja, Abhinav Juneja, Ramani Kannan

• Publisher: CRC Press
• Year: 2021
• Print ISBN:
  9780367685423
• eBook ISBN:
  9781000455687
• Topic: Design
• Subtopic: Computer Science General

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques.

This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making.

Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.

1 Random Variables in Machine Learning

Piratla Srihari

Geethanjali College of Engineering and Technology

DOI: 10.1201/9781003138020-1

CONTENTS

1. 1.1 Introduction
2. 1.2 Random Variable
   1. 1.2.1 Definition and Classification
      1. 1.2.1.1 Applications in Machine Learning
   2. 1.2.2 Describing a Random Variable in Terms of Probabilities
      1. 1.2.2.1 Ambiguity with Reference to Continuous Random Variable
   3. 1.2.3 Probability Density Function
      1. 1.2.3.1 Properties of pdf
      2. 1.2.3.2 Applications in Machine Learning
3. 1.3 Various Random Variables Used in Machine Learning
   1. 1.3.1 Continuous Random Variables
      1. 1.3.1.1 Uniform Random Variable
      2. 1.3.1.2 Gaussian (Normal) Random Variable
   2. 1.3.2 Discrete Random Variables
      1. 1.3.2.1 Bernoulli Random Variable
      2. 1.3.2.2 Binomial Random Variable
      3. 1.3.2.3 Poisson Random Variable
4. 1.4 Moments of Random Variable
   1. 1.4.1 Moments about Origin
      1. 1.4.1.1 Applications in Machine Learning
   2. 1.4.2 Moments about Mean
      1. 1.4.2.1 Applications in Machine Learning
5. 1.5 Standardized Random Variable
   1. 1.5.1 Applications in Machine Learning
6. 1.6 Multiple Random Variables
   1. 1.6.1 Joint Random Variables
      1. 1.6.1.1 Joint Cumulative Distribution Function (Joint CDF)
      2. 1.6.1.2 Joint Probability Density Function (Joint pdf)
      3. 1.6.1.3 Statistically Independent Random Variables
      4. 1.6.1.4 Density of Sum of Independent Random Variables
      5. 1.6.1.5 Central Limit Theorem
      6. 1.6.1.6 Joint Moments of Random Variables
      7. 1.6.1.7 Conditional Probability and Conditional Density Function of Random Variables
7. 1.7 Transformation of Random Variables
   1. 1.7.1 Applications in Machine Learning
8. 1.8 Conclusion
9. References

1.1 Introduction

Predicting the future using the knowledge about the past is the fundamental objective of machine learning.

In a digital communication system, a binary data generation scheme referred to as differential pulse code modulation (DPCM) works on the similar principle, where, based on the past behaviour of the signal, its future value will be predicted, using a predictor. A tapped delay line filter serves the purpose. More is the order of the predictor, better is the prediction, i.e. less is the prediction error.^[1]

Thus, machine learning, even though not being referred to by thi...

Table of contents

• Cover
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Editors
• Contributors
• Chapter 1 Random Variables in Machine Learning
• Chapter 2 Analysis of EMG Signals using Extreme Learning Machine with Nature Inspired Feature Selection Techniques
• Chapter 3 Detection of Breast Cancer by Using Various Machine Learning and Deep Learning Algorithms
• Chapter 4 Assessing the Radial Efficiency Performance of Bus Transport Sector Using Data Envelopment Analysis
• Chapter 5 Weight-Based Codes—A Binary Error Control Coding Scheme—A Machine Learning Approach
• Chapter 6 Massive Data Classification of Brain Tumors Using DNN: Opportunity in Medical Healthcare 4.0 through Sensors
• Chapter 7 Deep Learning Approach for Traffic Sign Recognition on Embedded Systems
• Chapter 8 Lung Cancer Risk Stratification Using ML and AI on Sensor-Based IoT: An Increasing Technological Trend for Health of Humanity
• Chapter 9 Statistical Feedback Evaluation System
• Chapter 10 Emission of Herbal Woods to Deal with Pollution and Diseases: Pandemic-Based Threats
• Chapter 11 Artificial Neural Networks: A Comprehensive Review
• Chapter 12 A Case Study on Machine Learning to Predict the Students’ Result in Higher Education
• Chapter 13 Data Analytic Approach for Assessment Status of Awareness of Tuberculosis in Nigeria
• Chapter 14 Active Learning from an Imbalanced Dataset: A Study Conducted on the Depression, Anxiety, and Stress Dataset
• Chapter 15 Classification of the Magnetic Resonance Imaging of the Brain Tumor Using the Residual Neural Network Framework
• Index