Soft computing and machine learning with Python

✍ Scribed by Zoran Gacovski

• Publisher: Arcler Press
• Year: 2019
• Tongue: English
• Leaves: 380
• Category: Library

✦ Table of Contents

Cover
Half Title Page
Title Page
Copyright Page
Declaration
About the Editor
Table of Contents
List of Contributors
List of Abbreviations
Preface
SECTION I SOFT COMPUTING THEORY
Chapter 1 Machine Learning Overview
Machine Learning Overview
References
Chapter 2 Types of Machine Learning Algorithms
Machine Learning: Algorithms Types
References
Chapter 3 Data Mining With Skewed Data
Introduction
Data Preparation
Data Skewness
Derived Characteristics
Categorisation (Grouping)
Sampling
Characteristics Selection
Objective Functions
Bottom Line Expected Prediction
Limited Resource Situation
Parametric Optimisation
Robustness of Parameters
Model Stability
Final Remarks
References
SECTION II MACHINE LEARNING TECHNIQUES AND APPLICATIONS
Chapter 4 Survey of Machine Learning Algorithms For Disease Diagnostic
Abstract
Introduction
Diagnosis of Diseases by Using Different Machine Learning
Algorithms
Discussions And Analysis Of Machine Learning Techniques
Conclusion
References
Chapter 5 Bankruptcy Prediction Using Machine Learning
Abstract
Introduction
Motivation
Related Work
Model Description
Experimental Result
Conclusions
References
Chapter 6 Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm
Abstract
Introduction
Background Information
Implementation
Results And Discussion
Conclusions
References
Chapter 7 Predicting Academic Achievement of High-School Students Using Machine Learning
Abstract
Introduction
Method
Results
Discussion
Conclusion
Acknowledgements
References
SECTION III PYTHON LANGUAGE DETAILS
Chapter 8 A Python 2.7 Programming Tutorial
Introduction
Python’s Numeric Types
Character String Basics
Sequence Types
Dictionaries
Branching
How To Write A Self-Executing Python Script
Using Python Modules
Input And Output
Introduction To Object-Oriented Programming
Chapter 9 Pattern For Python
Abstract
Introduction
Package Overview
Example Script
Case Study
Documentation
Source Code
Acknowledgments
References
Chapter 10 Pystruct - Learning Structured Prediction In Python
Abstract
Structured Prediction And Casting It Into Software
Usage Example: Semantic Image Segmentation
Experiments
Conclusion
Acknowledgments
References
SECTION IV MACHINE LEARNING WITH PYTHON
Chapter 11 Python Environment For Bayesian Learning: Inferring The Structure of Bayesian Networks From Knowledge And Data
Abstract
Introduction
PEBL Features
PEBL Development
Related Software
Conclusion And Future Work
Acknowledgments
References
Chapter 12 Scikit-Learn: Machine Learning In Python
Abstract
Introduction
Project Vision
Underlying Technologies
Code Design
High-Level Yet Efficient: Some Trade Offs
Conclusion
References
Chapter 13 An Efficient Platform For The Automatic Extraction of Patterns in Native Code
Abstract
Introduction
Motivating Example
Platform Architecture
Evaluation
Related Work
Conclusions
Acknowledgments
References
Chapter 14 Polyglot Programming In Applications Used For Genetic Data Analysis
Abstract
Background
Results
Discussion
Conclusion
Acknowledgments
References
Chapter 15 Classifying Multigraph Models Of Secondary RNA Structure Using Graph-Theoretic Descriptors
Abstract
Introduction
Graph-Theoretic Measures For The Dual Graphs
Assessing The Graph-Theoretic Measures as Descriptors of RNA Topology
Results
Conclusion
References
Index