Discriminant analysis and statistical pattern recognition by Geoffrey J. McLachlan

Discriminant analysis and statistical pattern recognition Geoffrey J. McLachlan ebook
Page: 544
Publisher: Wiley-Interscience
ISBN: 0471615315, 9780471615316
Format: djvu

337 samples were collected from three Partial least squares discriminant analysis (PLSDA) was used to develop classification models, and the influence of data preprocessing methods on classification performance was also investigated. December 23, 2012 | Author: voxjax | Posted in Business. Applied Regression Analysis, Linear Models, and Related Methods, Sage Publications; Hosmer, D. Discriminant Analysis and Statistical Pattern Recognition Book PDF. Linear Discriminant Analysis - Tools comparison. Discriminant Analysis and Statistical Pattern Recognition, Wiley-Interscience; Fox, J. The pattern recognition algorithm presented in this paper is a non parametric statistical pattern recognition algorithm base on a constrained k-means algorithm (Gordon, 1999). This paper develops a rapid method for discriminating the geographical origins and age of roasted Torreya grandis seeds by near infrared (NIR) spectroscopic analysis and pattern recognition. To compare the performance of the proposed technique the results of Fisher Discriminant Analysis (Duda and Hart, 1973) on the same data have been used. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, John Wiley and Sons, New York, 1992. The proposed technique will be tested in a simulation study. To be a valuable resource (and) should not be overlooked by any scholarly library. These contributions are best illustrated in his Wiley monographs, Discriminant Analysis and Statistical Pattern Recognition, The EM Algorithm and Extensions (with Thriyambakam Krishnan), and Finite Mixture Models (with David Peel). Linear discriminant analysis is a popular method in domains of statistics, machine learning and pattern recognition. In my opinion (this book) has been proved .