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Convex Analysisの二,三の進展について
徹 丸山
- Vol. 70, Iss: 1, pp 97-119
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The article was published on 1977-02-01 and is currently open access. It has received 5933 citations till now.read more
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References
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Journal ArticleDOI
Random Convex Programs
TL;DR: The relation between RCPVs and chance-constrained problems (CCP) is explored, showing that the optimal objective of an RCPV with the generic constraint removal rule provides, with arbitrarily high probability, an upper bound on the optimal objectives of a corresponding CCP.
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On the Consistency of Multiclass Classification Methods
Ambuj Tewari,Peter L. Bartlett +1 more
TL;DR: It turns out that one can lose consistency in generalizing a binary classification method to deal with multiple classes, so a rich family of multiclass methods are studied to provide a necessary and sufficient condition for their consistency.
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Pac-bayesian generalisation error bounds for gaussian process classification
TL;DR: By applying the PAC-Bayesian theorem of McAllester (1999a), this paper proves distribution-free generalisation error bounds for a wide range of approximate Bayesian GP classification techniques, giving a strong learning-theoretical justification for the use of these techniques.
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Optimal control of a class of hybrid systems
TL;DR: A modeling framework for hybrid systems intended to capture the interaction of event-driven and time-driven dynamics and several properties of optimal state trajectories are identified which significantly simplify the task of obtaining explicit optimal control policies.
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Credit risk optimization with Conditional Value-at-Risk criterion
TL;DR: This paper examines a new approach for credit risk optimization based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value- at-Risks, also known as Mean Excess, Mean Shortfall, or Tail VaR.