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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150605
Doc. No : ET22397
Main Entry : Qingping Tao
Title Proper : MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Expanding the learning problems' input spaces to high-dimensional feature spaces canincrease expressiveness of the hypothesis class and thus may improve the performanceof linear threshold-based learning algorithms such as Perceptron and Winnow. However,since the number of features is dramatically increased, these algorithms will not run ef-ficiently unless special techniques are used. Such techniques include Monte Carlo ap-proaches, grouping strategies and kernels. We investigated these techniques and applied...-...,..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 : TL45606.pdf
Title and statement of responsibility and : MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES [Thesis]
 
 
 
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