A Multivariate Statistical Pattern Recognition System for Reactor Noise Analysis

An Evaluation of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi System and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The...

A Statistical Pattern Recognition Framework for Noiserecognition in an Intelligent Noise Monitoring System

INTRODUCTION Actual noise monitoring systems have the shortcoming that although the intensity , duration, and time of occurrence of noises can be recorded, their source often cannot be identiied. Such information would be particularly useful when multiple noise sources are possible. This has led to research directed toward providing an \intelligent" noise monitoring system (Fig. 1) able to dist...

Multivariate pattern recognition analysis: brain decoding

Two of the most fundamental questions in the field of neurosciences are how information is represented in different brain structures, and how this information evolves over time. Various tools, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), have been developed over the last few decades to record brain activity and investigate these questions. In particular, func...

A Web-based Learning System for Statistical Pattern Recognition

This paper presents a web-based learning system in support of a Ph.D. course in Statistical Pattern Recognition. More specifically, the learning system allows the user to know the main concepts related with several distance-based classification and prototype selection algorithms, as well as to interactively experiment with them by means of Java applets. The aim of such a web-based learning syst...

Statistical Pattern Recognition Statistical Pattern Recognition Second Edition