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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150818
Doc. No : ET22610
Main Entry : Andres Sebastian Orrego
Title Proper : SAWTOOTH: Learning from Huge Amounts of Data
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Data scarcity has been a problem in data mining up until recent times. Now,in the era of the Internet and the tremendous advances in both, data storagedevices and high-speed computing, databases are filling up at rates neverimagined before. The machine learning problems of the past have been augmentedby an increasingly important one, scalability. Extracting usefulinformation from arbitrarily large data collections or data streams is now ofspecial interest within the data mining community. In this research we findthat mining from such large datasets may actually be quite simple. We addressthe scalability issues of previous widely-used batch learning algorithmsand discretization techniques used to handle continuous values within thedata. Then, we describe an incremental algorithm that addresses the scalabilityproblem of Bayesian classifiers, and propose a Bayesian-compatibleon-line discretization technique that handles continuous values, both with aب‍simplicity firstپ approach and very low memory (RAM) requirements.....-...,..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 : TL45834.pdf
Title and statement of responsibility and : SAWTOOTH: Learning from Huge Amounts of Data [Thesis]
 
 
 
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