An accompanying manual to Theodoridis/Koutroumbas, Pattern Recognition, that 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.
Introduction to Pattern Recognition: A Matlab Approach
โ Scribed by Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras
- Publisher
- Academic Press
- Year
- 2010
- Tongue
- English
- Leaves
- 216
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
I have read the first chapter so far, however i have found that the book is so practical. I have passed machine learning in the school and i can say that this book a good snapshot of that subject. The most important thing is that the book includes lots of matlab codes which really help the readers understand the concept of the application of different classifiers in a practical way by solving vary applicable examples throughout the book.
๐ SIMILAR VOLUMES
<i>Introduction to Pattern Recognition: A Matlab Approach</i>is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition.<br /><br />It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including rea
An accompanying manual to Theodoridis/Koutroumbas, Pattern Recognition, that 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.
<p><i>Introduction to Audio Analysis</i> serves as a standalone introduction to audio analysis, providing theoretical background to many state-of-the-art techniques. It covers the essential theory necessary to develop audio engineering applications, but also uses programming techniques, notably MATL
This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approa
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-d