Statistical Pattern Recognition for Breast Cancer Research: Comparison of Theory Driven General Linear Model Methodologies with Data Driven Artificial Neural Network

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" Statistical Pattern Recognition for Breast Cancer Research: Comparison of Theory Driven General Linear Model Methodologies with Data Driven Artificial Neural Network "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	149780
Doc. No	:	ET21572
Main Entry	:	Thomas D. Parsons
Title Proper	:	Statistical Pattern Recognition for Breast Cancer Research: Comparison of Theory Driven General Linear Model Methodologies with Data Driven Artificial Neural Network
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	Attempts at pattern-recognition in medical informaticdatabases have been limited by situations in which optimalalgorithms are difficult to ascertain. Data drivenArtificial Neural Networks (ANN) and theory driven GeneralLinear models (GLM) may be combined to develop a pragmaticapproach, which makes judicious use of both methodologies.The research reported showedtested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL44746.pdf
Title and statement of responsibility and	:	Statistical Pattern Recognition for Breast Cancer Research: Comparison of Theory Driven General Linear Model Methodologies with Data Driven Artificial Neural Network [Thesis]