Pattern Recognition Approach to Data Interpretation
β Scribed by Diane D. Wolff, Michael L. Parsons (auth.)
- Publisher
- Springer US
- Year
- 1983
- Tongue
- English
- Leaves
- 225
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when conΒ fronted with large data sets incorporating many parameters. A minimal amount of comΒ puter knowledge is necessary for successful applications, and we have tried conscienΒ tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sciΒ entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmenΒ tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition techΒ niques are essentially unlimited, restricted only by the outer limitations of.
β¦ Table of Contents
Front Matter....Pages i-xiii
Philosophical Considerations and Computer Packages....Pages 1-15
Pattern Recognition Approach to Data Analysis....Pages 17-108
Implementation....Pages 109-160
Natural Science Applications....Pages 161-172
Back Matter....Pages 173-223
β¦ Subjects
Pattern Recognition; Computer Applications in Chemistry
π SIMILAR VOLUMES
This 2nd edition is an update of the book "Wavelet Theory and Its Application to Pattern Recognition" published in 2000. Three new chapters, which are research results conducted during 2001-2008, will be added. The book consists of two parts - the first contains the basic theory of wavelet analysis
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.
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
<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