# LIONbook: Machine Learning + Intelligent Optimization – completed, free personal download

This book combines two usually separated topics: machine learning and intelligent optimization, and does it with enough technical details to satisfy professionals, but also with concrete examples, vivid images, and fun. Buy a low-cost paperback or ebook (Kindle), or download a free PDF.

By Gregory Piatetsky, Mar 11, 2014.

This book aims to combine two usually separated topics:

Both topics are technical and the book has appropriate equations to satisfy the analytics professionals. However, this book can also be read by non-professionals who want to understand the paradigm shift brought by machine learning and intelligent optimization methods. The book has plenty of concrete examples and vivid illustratons, and is fun to read!

The authors made this book freely available on the web for personal use at

intelligent-optimization.org/LIONbook/index.html

You can also buy a low-cost paperback version from Amazon or download ebook (Kindle format).

1 Introduction 1

2 Lazy learning: nearest neighbors 9

3 Learning requires a method 15

4 Linear models 29

5 Mastering generalized linear least-squares 41

6 Rules, decision trees, and forests 59

7 Ranking and selecting features 71

8 Specific nonlinear models 81

9 Neural networks, shallow and deep 93

10 Statistical Learning Theory and Support Vector Machines (SVM) 109

11 Democracy in machine learning 123

12 Top-down clustering: K-means 137

13 Bottom-up (agglomerative) clustering 149

14 Self-organizing maps 157

15 Dimensionality reduction by linear transformations (projections) 165

16 Visualizing graphs and networks by nonlinear maps 179

17 Semi-supervised learning 191

18 Automated improvements by local steps 203

19 Local Search and Reactive Search Optimization (RSO) 235

20 Continuous and Cooperative Reactive Search Optimization (CoRSO) 251

21 Multi-Objective Reactive Search Optimization (MORSO) 265

22 Text and web mining 277

23 Collaborative filtering and recommendation 299

Bibliography 307

**LIONbook**, is a new, just completed book, written by the developers of LionSolver software, Roberto Battiti and Mauro Brunato.This book aims to combine two usually separated topics:

- machine learning
- and intelligent optimization.

Both topics are technical and the book has appropriate equations to satisfy the analytics professionals. However, this book can also be read by non-professionals who want to understand the paradigm shift brought by machine learning and intelligent optimization methods. The book has plenty of concrete examples and vivid illustratons, and is fun to read!

The authors made this book freely available on the web for personal use at

intelligent-optimization.org/LIONbook/index.html

You can also buy a low-cost paperback version from Amazon or download ebook (Kindle format).

**Contents:**1 Introduction 1

2 Lazy learning: nearest neighbors 9

3 Learning requires a method 15

4 Linear models 29

5 Mastering generalized linear least-squares 41

6 Rules, decision trees, and forests 59

7 Ranking and selecting features 71

8 Specific nonlinear models 81

9 Neural networks, shallow and deep 93

10 Statistical Learning Theory and Support Vector Machines (SVM) 109

11 Democracy in machine learning 123

12 Top-down clustering: K-means 137

13 Bottom-up (agglomerative) clustering 149

14 Self-organizing maps 157

15 Dimensionality reduction by linear transformations (projections) 165

16 Visualizing graphs and networks by nonlinear maps 179

17 Semi-supervised learning 191

18 Automated improvements by local steps 203

19 Local Search and Reactive Search Optimization (RSO) 235

20 Continuous and Cooperative Reactive Search Optimization (CoRSO) 251

21 Multi-Objective Reactive Search Optimization (MORSO) 265

22 Text and web mining 277

23 Collaborative filtering and recommendation 299

Bibliography 307