Parametric Classification
In this chapter and the next, we discuss simple classification rules that are based on estimating the feature-label distribution from the data. If ignorance about the distribution is confined to a few numerical parameters, then these algorithms are called
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Fundamentals of Pattern Recognition and Machine Learning
Fundamentals of Pattern Recognition and Machine Learning
Ulisses Braga-Neto
Fundamentals of Pattern Recognition and Machine Learning
Ulisses Braga-Neto Department of Electrical and Computer Engineering Texas A&M University College Station, TX, USA
ISBN 978-3-030-27655-3 ISBN 978-3-030-27656-0 (eBook) https://doi.org/10.1007/978-3-030-27656-0 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
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Preface “Only the educated are free.” –Epictetus.
The field of pattern recognition and machine learning has a long and distinguished history. In particular, there are many excellent textbooks on the topic, so the question of why a new textbook is desirable must be confronted. The goal of this book is to be a concise introduction, which combines theory and practice and is suitable to the classroom. It includes updates on recent methods and examples of applications based on the python programming language. The book does not attempt an encyclopedic treatment of pattern recognition and machine learning, which has become impossible in any case, due to how much the field has grown. A stringent selection of material is mandatory for a concise textbook, and the choice of topics made here, while dictated to a certain extent by my own experience and preferences, is believed to equip the reader with the core knowledge one must obtain to be proficient in this field. Calculus and probability at the undergraduate level are the minimum prerequisites for the book. The appendices contain short reviews of probability at the graduate level and other mathematical tools that are needed in the text. This book ha
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