
Introduction to Pattern Recognition
A Matlab Approach
- 231 pages
- English
- PDF
- Available on iOS & Android
Introduction to Pattern Recognition
A Matlab Approach
About this book
Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition.It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.- Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition- Solved examples in Matlab, including real-life data sets in imaging and audio recognition- Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
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Information
Table of contents
- Front Cover
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Chapter 1. Classifiers Based on Bayes Decision Theory
- Chapter 2. Classifiers Based on Cost Function Optimization
- Chapter 3. Data Transformation: Feature Generation and Dimensionality Reduction
- Chapter 4. Feature Selection
- Chapter 5. Template Matching
- Chapter 6. Hidden Markov Models
- Chapter 7. Clustering
- Appendix
- References
- Index