Soft computing and machine learning with Python

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