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Computer Manual in MATLAB to Accompany Pattern Classification, 2nd Edition
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A complete MATLAB toolbox to accompany Pattern Classification
Second Edition
Pattern classification is a vital and growing field with applications in such
areas as speech recognition, handwriting recognition, computer vision, image
analysis, data mining, information retrieval, machine learning and neural networks.
Expanding on the MATLAB classification toolbox developed by Elad Yom-Tov at
the Technion, Israel Institute of Technology, and tested by hundreds of students
and practioners worldwide, Computer Manual in MATLAB to accompany Pattern Classification,
Second Edition, serves as both a companion to Pattern Classification, Second
Edition, and as a professional software toolbox for researchers in pattern classification
and signal processing.
Beginning with an introduction to programming in MATLAB suitable for readers
with no such programming experience, this Manual and its accompanying software:
- Implement all the algorithms described in Pattern Classification, Second
Edition
- Implement important recent algorithms not found in the text
- Use the same terminology as the text
- Include representative data sets, including those from the computer exercises
in the text
- Include step-by-step worked examples, including some of the examples and
figures in the text
- Provide self-annotated code so the user can easily navigate, understand,
and modify the code
- Offer priviledged access to an associated Wiley ftp site for downloading
all the software, corrections and additions
Table of Contents
Preface.
Chapter 1. Introduction to MATLAB.
Basic Navigation and Interaction.
Scalars, Variables and Basic Arithmetic.
Relational and Logical Operators.
Lists, Vectors and Matrices.
Matrix Multiplication.
Vector and Matrix Norms.
Determinants, Inverses and Pseudoinverses.
Matrix Powers and Exponentials.
Eigenvalues and Eigenvectors.
Data Analysis.
Clearing Variables and Functions.
Data Types.
Chapter 2. Programming in MATLAB.
Scripts.
Functions.
Flow Control.
User Input.
Debugging.
Data, and File Input and Output.
Strings.
Operations on Strings.
Chapter 3. Classification Toolbox.
Loading the Toolbox and Starting MATLAB.
Graphical User Interface.
Introductory Examples.
GUI Controls.
Creating Your Own Data Files.
Classifying Using the Text-based Interface.
Classifier Comparisons.
How to Add New Algorithms.
Adding a New Feature Selection Algorithm.
List of Functions.
Appendix: Program Descriptions.
References.
Index.
About the Authors
DAVID G. STORK, PhD, is Chief Scientist at Ricoh Innovations and Consulting
Professor of Electrical Engineering at Stanford University. A graduate of MIT
and the University of Maryland, he is the founder and leader of the Open Mind
Initiative and the coauthor, with Richard Duda and Peter Hart, of Pattern Classification,
Second Edition, as well as four other books.
ELAD YOM-TOV, PhD, is a research scientist at IBM Haifa Research Laboratory,
working on the applications of machine learning to search technologies, bioinformatics,
and hardware verification (among others). He is a graduate of Tel-Aviv University
and the Technion.
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