Synopses & Reviews
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 privileged access to an associated Wiley ftp site for downloading all the software, corrections, and additions
Computer Manual to Accompany Pattern Classification and its associated MATLAB software is an excellent companion to Duda: Pattern Classfication, 2nd ed, (DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook.
The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code.
About the Author
DAVID G. STORK, PhD, is Chief Scientist at Ricoh Innovations, Inc., 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 Research Lab in Haifa, 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.
Table of Contents
Chapter 1. Introduction to MATLAB.
Basic Navigation and Interaction.
Scalars, Variables and Basic Arithmetic.
Relational and Logical Operators.
Lists, Vectors and Matrices.
Vector and Matrix Norms.
Determinants, Inverses and Pseudoinverses.
Matrix Powers and Exponentials.
Eigenvalues and Eigenvectors.
Clearing Variables and Functions.
Chapter 2. Programming in MATLAB.
Data, and File Input and Output.
Operations on Strings.
Chapter 3. Classification Toolbox.
Loading the Toolbox and Starting MATLAB.
Graphical User Interface.
Creating Your Own Data Files.
Classifying Using the Text-based Interface.
How to Add New Algorithms.
Adding a New Feature Selection Algorithm.
List of Functions.
Appendix: Program Descriptions.