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pro vyhledávání: '"Ghanbari, Hiva"'
The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Minimizing the reciprocals of these measures are the goals of supervised
Externí odkaz:
http://arxiv.org/abs/1903.00359
Autor:
Ghanbari, Hiva, Scheinberg, Katya
The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction error or the so-called Area Under the Curve (AUC) for a particular data distribution. However, when the models are constructed
Externí odkaz:
http://arxiv.org/abs/1802.02535
Autor:
Ghanbari, Hiva, Scheinberg, Katya
In this work, we utilize a Trust Region based Derivative Free Optimization (DFO-TR) method to directly maximize the Area Under Receiver Operating Characteristic Curve (AUC), which is a nonsmooth, noisy function. We show that AUC is a smooth function,
Externí odkaz:
http://arxiv.org/abs/1703.06925
Autor:
Ghanbari, Hiva, Scheinberg, Katya
In [19], a general, inexact, efficient proximal quasi-Newton algorithm for composite optimization problems has been proposed and a sublinear global convergence rate has been established. In this paper, we analyze the convergence properties of this me
Externí odkaz:
http://arxiv.org/abs/1607.03081
Akademický článek
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Autor:
Ma, Yuntao, Ghanbari, Hiva, Huang, Tianyuan, Irvin, Jeremy, Brady, Oliver, Zalouk, Sofian, Sheng, Hao, Ng, Andrew, Rajagopal, Ram, Narsude, Mayur
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems; 2024, Vol. 25 Issue: 6 p5627-5639, 13p
Autor:
Ghanbari, Hiva1 hiva.ghanbari@gmail.com, Scheinberg, Katya1 katyascheinberg@gmail.com
Publikováno v:
Computational Optimization & Applications. Apr2018, Vol. 69 Issue 3, p597-627. 31p.