Results 11 to 20 of about 393,254 (286)
Opportunity Loss Minimization and Newsvendor Behavior [PDF]
Discrete Dynamics in Nature and Society, 2017 To study the decision bias in newsvendor behavior, this paper introduces an opportunity loss minimization criterion into the newsvendor model with backordering.
Xinsheng Xu, Hong Yan, Chi Kin Chan
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Optimal Minimization of the Covariance Loss
IEEE Transactions on Information Theory, 2023 Let $X$ be a random vector valued in $\mathbb{R}^{m}$ such that $\|X\|_{2} \le 1$ almost surely. For every $k\ge 3$, we show that there exists a sigma algebra $\mathcal{F}$ generated by a partition of $\mathbb{R}^{m}$ into $k$ sets such that \[\|\operatorname{Cov}(X) - \operatorname{Cov}(\mathbb{E}[X\mid\mathcal{F}]) \|_{\mathrm{F}} \lesssim \frac{1 ...
Vishesh Jain +2 more
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STCMH with minimal semantic loss [PDF]
IET Image Processing, 2019 Cross‐modal hashing (CMH) has received widespread attention due to high retrieval efficiency, which plays an extremely important role in cross‐modal retrieval. Recently, many CMH methods have been proposed to establish the semantic connection of different modalities.
Jianing Du +3 more
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Analytic Loss Minimization: A Proof [PDF]
IEEE Transactions on Power Systems, 2016 Abstract—Loss minimizing generator dispatch profiles for power systems are usually derived using optimization techniques. However, some authors have noted that a system’s KGL matrix can be used to analytically determine a loss minimizing dispatch. This letter draws on recent research on the characterization of transmission system losses to demonstrate ...
Paul Cuffe +2 more
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Loss minimization in parse reranking [PDF]
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06, 2006 We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. Different approaches to expected loss approximation are considered, including estimating from the probabilistic model used to generate the candidates, estimating from a discriminative model ...
Ivan Titov 0001, James Henderson 0001
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On Data Preconditioning for Regularized Loss Minimization [PDF]
Machine Learning, 2015 In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization. It is well understood that the condition number of the problem, i.e., the ratio of the Lipschitz constant to the strong convexity modulus, has a harsh effect on the convergence of ...
Tianbao Yang +3 more
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IEEE Access, 2021 The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints.
Meisam Mahdavi +4 more
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Core loss resistance impact on sensorless speed control of an induction motor using hybrid adaptive sliding mode observer [PDF]
Archives of Electrical Engineering, 2023 Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses.
Tadele Ayana +2 more
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Making Risk Minimization Tolerant to Label Noise [PDF]
, 2015 In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in presence of label noise.
Ghosh, Aritra +2 more
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Energies, 2018 This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered.
Zahir Sahli +3 more
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