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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150523
Doc. No : ET22315
Main Entry : ALEXANDER NOVOKHODKO
Title Proper : NEURAL NETWORKS WITH CATEGORICAL VALUED INPUTS AND APPLICATIONS TO INTRUSION DETECTION
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Neural networks, a powerful machine learning paradigm, have been successfullyapplied to a wide spectrum of practical problems. Being universal approximators, theneural networks work best with quantitative inputs, when different values of an inputindicate different magnitudes (e.g., intensity). For ordinal inputs, when different values ofan input signify order relation (e.g., position in a sequence), the neural networks are stillable to do a decent job, but for categorical inputs, when different values of an input-...,..tested 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 : TL45521.pdf
Title and statement of responsibility and : NEURAL NETWORKS WITH CATEGORICAL VALUED INPUTS AND APPLICATIONS TO INTRUSION DETECTION [Thesis]
 
 
 
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