Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization
<p> Investigated into and motivated by Ensemble Machine Learning (<i>ML</i>) techniques, this thesis contributes to addressing performance, consistency, and integrity issues such as overfitting, underfitting, predictive errors, accuracy paradox, and poor generalization for the <...
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| Language: | EN |
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University of Bridgeport
2019
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| Online Access: | http://pqdtopen.proquest.com/#viewpdf?dispub=13427950 |