Soft computing and machine learning with Python

Soft Computing and Machine Learning with Python examines various aspects of machine learning with python with a detailed information on soft computing. It includes four different sections, where section 1 and 2 are dedicated towards soft computing theory and machine learning techniques and on the other hand section 3 and 4 are dedicated to the details of python language and machine learning with python. Provides the reader with the insights into the development of python and machine learning, so as to understand the classification multigraph models of secondary RNA structure using graph-theoretic descriptors. Cover 1 Half Title Page 3 Title Page 5 Copyright Page 6 Declaration 7 About the Editor 9 Table of Contents 11 List of Contributors 17 List of Abbreviations 23 Preface 25 SECTION I SOFT COMPUTING THEORY 27 Chapter 1 Machine Learning Overview 29 Machine Learning Overview 29 References 40 Chapter 2 Types of Machine Learning Algorithms 45 Machine Learning: Algorithms Types 45 References 78 Chapter 3 Data Mining With Skewed Data 83 Introduction 84 Data Preparation 84 Data Skewness 87 Derived Characteristics 88 Categorisation (Grouping) 89 Sampling 92 Characteristics Selection 93 Objective Functions 93 Bottom Line Expected Prediction 94 Limited Resource Situation 94 Parametric Optimisation 95 Robustness of Parameters 96 Model Stability 99 Final Remarks 101 References 102 SECTION II MACHINE LEARNING TECHNIQUES AND APPLICATIONS 105 Chapter 4 Survey of Machine Learning Algorithms For Disease Diagnostic 107 Abstract 107 Introduction 108 Diagnosis of Diseases by Using Different Machine Learning Algorithms 111 Discussions And Analysis Of Machine Learning Techniques 123 Conclusion 124 References 126 Chapter 5 Bankruptcy Prediction Using Machine Learning 129 Abstract 129 Introduction 130 Motivation 131 Related Work 132 Model Description 133 Experimental Result 138 Conclusions 139 References 143 Chapter 6 Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm 145 Abstract 145 Introduction 146 Background Information 147 Implementation 150 Results And Discussion 151 Conclusions 152 References 155 Chapter 7 Predicting Academic Achievement of High-School Students Using Machine Learning 157 Abstract 157 Introduction 158 Method 164 Results 169 Discussion 171 Conclusion 174 Acknowledgements 174 References 175 SECTION III PYTHON LANGUAGE DETAILS 179 Chapter 8 A Python 2.7 Programming Tutorial 181 Introduction 181 Python’s Numeric Types 182 Character String Basics 191 Sequence Types 207 Dictionaries 219 Branching 225 How To Write A Self-Executing Python Script 235 Using Python Modules 243 Input And Output 250 Introduction To Object-Oriented Programming 255 Chapter 9 Pattern For Python 273 Abstract 273 Introduction 274 Package Overview 275 Example Script 276 Case Study 277 Documentation 277 Source Code 277 Acknowledgments 278 References 279 Chapter 10 Pystruct - Learning Structured Prediction In Python 281 Abstract 281 Structured Prediction And Casting It Into Software 282 Usage Example: Semantic Image Segmentation 284 Experiments 285 Conclusion 286 Acknowledgments 286 References 287 SECTION IV MACHINE LEARNING WITH PYTHON 289 Chapter 11 Python Environment For Bayesian Learning: Inferring The Structure of Bayesian Networks From Knowledge And Data 291 Abstract 291 Introduction 292 PEBL Features 292 PEBL Development 295 Related Software 295 Conclusion And Future Work 295 Acknowledgments 295 References 296 Chapter 12 Scikit-Learn: Machine Learning In Python 297 Abstract 298 Introduction 298 Project Vision 299 Underlying Technologies 299 Code Design 300 High-Level Yet Efficient: Some Trade Offs 301 Conclusion 302 References 303 Chapter 13 An Efficient Platform For The Automatic Extraction of Patterns in Native Code 305 Abstract 305 Introduction 306 Motivating Example 308 Platform Architecture 310 Evaluation 322 Related Work 332 Conclusions 333 Acknowledgments 334 References 335 Chapter 14 Polyglot Programming In Applications Used For Genetic Data Analysis 339 Abstract 339 Background 340 Results 341 Discussion 350 Conclusion 350 Acknowledgments 351 References 352 Chapter 15 Classifying Multigraph Models Of Secondary RNA Structure Using Graph-Theoretic Descriptors 355 Abstract 355 Introduction 356 Graph-Theoretic Measures For The Dual Graphs 360 Assessing The Graph-Theoretic Measures as Descriptors of RNA Topology 362 Results 364 Conclusion 372 References 374 Index 377

Soft computing and machine learning with python examines various aspects of machinelearning with python with a detailed information on soft computing. It includes fourdifferent sections, where section 1 and 2 are dedicated towards soft computing theoryand machine learning techniques and on the other hand section 3 and 4 are dedicatedto the details of python language and machine learning with python. The book providesthe reader with the insights into the development of python and machine learning, soas to understand the classification multigraph models of secondary RNA structure usinggraph-theoretic descriptors.

Examines various aspects of machine learning with python, including detailed information on soft computing. Coverage includes soft computing theory and machine learning techniques, the python language, and machine learning with python.