Winters-Hilt Stephen - Informatics and Machine Learning : From Martingales to Metaheuristics

1. Chapter 3
2. Chapter 5
3. Chapter 10
4. Chapter 14

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 6
6. Chapter 7
7. Chapter 8
8. Chapter 9
9. Chapter 10
10. Chapter 11
11. Chapter 12
12. Chapter 13
13. Chapter 14

Stephen WintersHilt

Meta Logos Systems

Albuquerque, NM, USA

This edition first published 2022
2022 John Wiley and Sons, Inc.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Stephen WintersHilt to be identified as the author of this work has been asserted in accordance with law.

Registered Office
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

Editorial Office
111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by printondemand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of Warranty
The contents of this work are intended to further general scientific research, understanding, and discussion only and are not intended and should not be relied upon as recommending or promoting scientific method, diagnosis, or treatment by physicians for any particular patient. In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of medicines, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each medicine, equipment, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress CataloginginPublication Data applied for:

ISBN: 9781119716747

Cover Design: Wiley
Cover Image: agsandrew/Shutterstock

This book is dedicated to my family: Cindy, Nathaniel, Zachary, Sybil, Teresa, Eric, and Josh.

The material in this book draws from undergraduate and graduate coursework taken while I was a student at Caltech, and from further graduate coursework and studies at Oxford, University of Wisconsin, and University of California, Santa Cruz. The material also draws upon my teaching experience and research efforts while a tenured professor of computer science at the University of New Orleans and jointly appointed as principal investigator and director of a protein channel biosensor lab at the Research Institute for Children at Childrens Hospital in New Orleans.

Informatics provides new avenues of understanding and inquiry in any medium that can be captured in digital form. Areas as diverse as text analysis, signal analysis, and genome analysis, to name a few, can be studied with informatics tools. Computationally powered informatics tools are having a phenomenal impact in many fields, including engineering, nanotechnology, and the biological sciences ().

In this text I provide a background on various methods from Informatics and Machine Learning ( ML ) that together comprise a complete toolset for doing data analytics work at all levels from a first year undergraduate introductory level to advanced topics in subsections suitable for graduate students seeking a deeper understanding (or a more detailed example). Numerous prior book, journal, and patent publications by the author are drawn upon extensively throughout the text [] provide related material, but are not directly drawn upon this text. The application domain is practically everything in the digital domain, as mentioned above, but in this text the focus will be on core methodologies with specific application in informatics, bioinformatics, and cheminformatics (nanopore detection, in particular). Other disciplines can also be analyzed with informatics tools. Basic questions about human origins (anthrogenomics) and behavior (econometrics) can also be explored with informaticsbased pattern recognition methods, with a huge impact on new research directions in anthropology, sociology, political science, economics, and psychology. The complete toolset of statistical learning tools can be used in any of these domains.

In the chapter that follows an overview is given of the various information processing stages to be discussed in the text, with some highlights to help explain the order and connectivity of topics, as well as motivate their presentation in further detail in what is to come.

A Penrose tiling. A nonrepeating tiling with two shapes of tiles, with 5point local symmetry and both local and global (emergent) golden ratio.

Knowledge construction using statistical and computational methods is at the heart of data science and informatics. Counts on data features (or events) are typically gathered as a starting point in many analyses [).

It is common to need to acquire a signal where the signal properties are not known, or the signal is only suspected and not discovered yet, or the signal properties are known but they may be too much trouble to fully enumerate. There is no common solution, however, to the acquisition task. For this reason the initial phases of acquisition methods unavoidably tend to be