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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150594
Doc. No : ET22386
Main Entry : BAOFU DUAN
Title Proper : ITERATIVE FEATURE WEIGHTING FOR IDENTIFICATION OF RELEVANT FEATURES IN MACHINE LEARNING: WITH MULTILAYER PERCEPTRON, RADIAL BASIS FUNCTION AND SUPPORT VECTOR ARCHITECTURES
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : In multivariate data analysis, samples may be described in terms of many features,but in specific tasks some features may be redundant or irrelevant, serving primarily assources of noise and confusion. The irrelevant and redundant features not only increasethe cost of data collection, but may also be the reason why machine learning is oftenhampered by lack of an adequate number of samples.Feature selection can be used to address the issue by identifying and selecting onlythose features that are relevant to the specific task in question. An alternate approach isfeature weighting which assigns continuous-..-...,..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 : TL45595.pdf
Title and statement of responsibility and : ITERATIVE FEATURE WEIGHTING FOR IDENTIFICATION OF RELEVANT FEATURES IN MACHINE LEARNING: WITH MULTILAYER PERCEPTRON, RADIAL BASIS FUNCTION AND SUPPORT VECTOR ARCHITECTURES [Thesis]
 
 
 
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