Spelling suggestions: "subject:"computing anda artificial intelligence"" "subject:"computing ando artificial intelligence""
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CRITICAL VALUES: FEMINIST PHILOSOPHY OF SCIENCE AND THE COMPUTING SCIENCESSHERRON, CATHERINE ELIZABETH 01 July 2003 (has links)
No description available.
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Analyse von Translationsstarts in prokaryotischen Genomen mit Methoden des Maschinellen Lernens / Analysis of translation initiation sites in prokaryotic genomes with machnine learning methodsTech, Maike 02 November 2007 (has links)
No description available.
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On the Value of Prediction and Feedback for Online Decision Making With Switching CostsMing Shi (12621637) 01 June 2022 (has links)
<p>Online decision making with switching costs has received considerable attention in many practical problems that face uncertainty in the inputs and key problem parameters. Because of the switching costs that penalize the change of decisions, making good online decisions under such uncertainty is known to be extremely challenging. This thesis aims at providing new online algorithms with strong performance guarantees to address this challenge.</p>
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<p>In part 1 and part 2 of this thesis, motivated by Network Functions Virtualization and smart grid, we study competitive online convex optimization with switching costs. Specifically, in part 1, we focus on the setting with an uncertainty set (one type of prediction) and hard infeasibility constraints. We develop new online algorithms that can attain optimized competitive ratios, while ensuring feasibility at all times. Moreover, we design a robustification procedure that helps these algorithms obtain good average-case performance simultaneously. In part 2, we focus on the setting with look-ahead (another type of prediction). We provide the first algorithm that attains a competitive ratio that not only decreases to 1 as the look-ahead window size increases, but also remains upper-bounded for any ratio between the switching-cost coefficient and service-cost coefficient.</p>
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<p>In part 3 of this thesis, motivated by edge computing with artificial intelligence, we study bandit learning with switching costs where, in addition to bandit feedback, full feedback can be requested at a cost. We show that, when only 1 arm can be chosen at a time, adding costly full-feedback is not helpful in fundamentally reducing the Θ(<em>T</em>2/3) regret over a time-horizon <em>T</em>. In contrast, when 2 (or more) arms can be chosen at a time, we provide a new online learning algorithm that achieves a significantly smaller regret equal to <em>O</em>(√<em>T</em>), without even using full feedback. To the best of our knowledge, this type of sharp transition from choosing 1 arm to choosing 2 (or more) arms has never been reported in the literature.</p>
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An Active Domain Node Architecture for the Semantic Web / Eine Knotenarchitektur mit aktivem Verhalten für das Semantic WebSchenk, Franz 21 November 2008 (has links)
No description available.
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Learning Finite State Machine Specifications from Test Cases / Lernen von Spezifikationen in Form von endlichen Zustandsmaschinen aus TestfällenWerner, Edith Benedicta Maria 01 June 2010 (has links)
No description available.
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Modellierung dynamischer Prozesse mit radialen Basisfunktionen / Modeling of dynamical processes using radial basis functionsDittmar, Jörg 20 August 2010 (has links)
No description available.
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Local- and Cluster Weighted Modeling for Prediction and State Estimation of Nonlinear Dynamical Systems / Lokale- und Cluster-Weighted-Modellierung zur Vorhersage und Zustandsschätzung nichtlinearer dynamischer SystemeEngster, David 24 August 2010 (has links)
No description available.
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