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dc.contributor.advisorRozenbilt, Jerzy W.en
dc.contributor.advisorHead, Kenneth L.en
dc.contributor.authorValenzuela, Michael Lawrence
dc.creatorValenzuela, Michael Lawrenceen
dc.date.accessioned2016-04-12T20:54:01Zen
dc.date.available2016-04-12T20:54:01Zen
dc.date.issued2016en
dc.identifier.urihttp://hdl.handle.net/10150/605111en
dc.description.abstractTraditionally the machine learning community has viewed the No Free Lunch (NFL) theorems for search and optimization as a limitation. I review, analyze, and unify the NFL theorem with the many frameworks to arrive at necessary conditions for improving black-box optimization, model selection, and machine learning in general. I review meta-learning literature to determine when and how meta-learning can benefit machine learning. We generalize meta-learning, in context of the NFL theorems, to arrive at a novel technique called Anti-Training with Sacrificial Data (ATSD). My technique applies at the meta level to arrive at domain specific algorithms and models. I also show how to generate sacrificial data. An extensive case study is presented along with simulated annealing results to demonstrate the efficacy of the ATSD method.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.subjectMachine Learningen
dc.subjectMeta Optimizationen
dc.subjectNo Free Lunchen
dc.subjectOptimizationen
dc.subjectSacrificial Dataen
dc.subjectElectrical & Computer Engineeringen
dc.subjectAnti-Trainingen
dc.titleMachine Learning, Optimization, and Anti-Training with Sacrificial Dataen_US
dc.typetexten
dc.typeElectronic Dissertationen
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.leveldoctoralen
dc.contributor.committeememberRozenbilt, Jerzy W.en
dc.contributor.committeememberHead, Kenneth L.en
dc.contributor.committeememberLysecky, Roman L.en
dc.contributor.committeememberMarcellin, Michael W.en
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineElectrical & Computer Engineeringen
thesis.degree.namePh.D.en
refterms.dateFOA2018-06-15T04:44:22Z
html.description.abstractTraditionally the machine learning community has viewed the No Free Lunch (NFL) theorems for search and optimization as a limitation. I review, analyze, and unify the NFL theorem with the many frameworks to arrive at necessary conditions for improving black-box optimization, model selection, and machine learning in general. I review meta-learning literature to determine when and how meta-learning can benefit machine learning. We generalize meta-learning, in context of the NFL theorems, to arrive at a novel technique called Anti-Training with Sacrificial Data (ATSD). My technique applies at the meta level to arrive at domain specific algorithms and models. I also show how to generate sacrificial data. An extensive case study is presented along with simulated annealing results to demonstrate the efficacy of the ATSD method.


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