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Novel active learning based approaches for balancing multi-objective maximization using trade-off between exploration and exploitation / Dina Ahmed Mohamed Mohamed Elreedy ; Supervised Samir I. Shaheen , Amir Fouad Surial Atiya

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Dina Ahmed Mohamed Mohamed Elreedy , 2020Description: 93 P. : charts , facimiles ; 25cmOther title:
  • طرق مبتكرة لاستخدام التعلم الفعال من أجل تحقيق التوازن لتعظيم الأهداف المتعددة باستخدام التوازن بين الاستكشاف والاستغلال [Added title page title]
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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: In this thesis, we develop two novel approaches for optimization problems incurring exploration-exploitation trade-off. First, we propose a new comprehensive active learning framework including exploration-based, exploitation-based, and balancing methods. Second, we develop several analytical formulations for handling exploration-exploitation trade-off by explicitly incorporating an exploration term depending on the learning model uncertainty. We apply our proposed approaches to an operations research related application which is dynamic pricing with demand learning. We perform experiments on synthetic and real datasets. The experimental results show superior performance of our proposed approaches in terms of the achieved utility (exploitation) and estimated model error (exploration)
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.Ph.D.2020.Di.N (Browse shelf(Opens below)) Not for loan 01010110081390000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.Ph.D.2020.Di.N (Browse shelf(Opens below)) 81390.CD Not for loan 01020110081390000

Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

In this thesis, we develop two novel approaches for optimization problems incurring exploration-exploitation trade-off. First, we propose a new comprehensive active learning framework including exploration-based, exploitation-based, and balancing methods. Second, we develop several analytical formulations for handling exploration-exploitation trade-off by explicitly incorporating an exploration term depending on the learning model uncertainty. We apply our proposed approaches to an operations research related application which is dynamic pricing with demand learning. We perform experiments on synthetic and real datasets. The experimental results show superior performance of our proposed approaches in terms of the achieved utility (exploitation) and estimated model error (exploration)

Issued also as CD

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