Results 131 to 140 of about 1,057,367 (187)

Mood and Cognitive Disorders Following Hearing Loss: Impact of Hearing Aid Timing. [PDF]

Audiol Res
Alberti G, Loteta S, Portelli D, Galletti C, Galletti F, Galletti B, Lentini M, Ronsivalle S, Maira S, Lechien JR, Gargula S, Maniaci A.   +11 more
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Radar HRRP Sequence Target Recognition Based on a Lightweight Spatiotemporal Fusion Network. [PDF]

Sensors (Basel)
Li X, Su Y, Zhao X, Yin J, Yang J.
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Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure. [PDF]

Open Heart
Qiu X, Ma J, Xu L, Jiang M, Pu J.
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Study on the Effect of Different Occlusal Schemes on Temporomandibular Joint Health in Complete Denture Wearers.

J Pharm Bioallied Sci
Sinha RR, Alekhya P, Srinu G, Kaur H, Neelavathi P, Langpoklakpam C.   +5 more
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An Appreciation of Balanced Loss Functions Via Regret Loss

Communications in Statistics - Theory and Methods, 2014
We examine balanced loss functions, which account for both estimation error and goodness of fit (or proximity to a “target” estimator), in terms of their regret losses, providing new insight and interpretations. This also shows a connection between quadratic balanced loss and usual quadratic loss, which easily converts frequentist and Bayesian results ...
Tapan K. Nayak, Bimal Sinha
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The optimal extended balanced loss function estimators

Journal of Computational and Applied Mathematics, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kaçıranlar S., Dawoud I.
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Generalized Liu Type Estimators Under Zellner's Balanced Loss Function

Communications in Statistics - Theory and Methods, 2005
ABSTRACT In regression analysis, ridge regression estimators and Liu type estimators are often used to overcome the problem of multicollinearity. These estimators have been evaluated using the risk under quadratic loss criterion, which places sole emphasis on estimators′ precision. The traditional mean square error (MSE) as the measure of efficiency of
Akdeniz F., Wan A.T.K., Akdeniz E.
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On estimation with weighted balanced-type loss function

Statistics & Probability Letters, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jozani, Mohammad Jafari, Marchand, Éric, Parsian, Ahmad   +2 more
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LINEAR ESTIMATORS OF A POISSON MEAN UNDER BALANCED LOSS FUNCTIONS

Statistics & Risk Modeling, 1998
Summary: This paper considers estimation of a Poisson mean using \textit{A. Zellner}'s [\textit{S.S. Gupta} et al. (eds.), Stat. Decision Theory Relat. Topics V. Proc. fifth Purdue Int. Symp., 377-390 (1994; Zbl 0787.62035)] balanced loss function \[ L_B(\lambda, \widehat\lambda) =(w/n) \sum^n_{i=1} (X_i-\widehat \lambda)^2+(1-w) (\lambda- \widehat ...
Chung, Younshik, Kim, Chansoo, Song, Seongho   +2 more
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Balance-batch: An Optimized Method for Semantic Segmentation Loss Functions

2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2020
Class-imbalanced data easily generates under-fitting problems in deep neural networks, which seriously limits the performance of the network. Several schemes have been proposed to alleviate the class-imbalance, i.e., data augmentation and network structure optimization. Our work has two main contributions: First, we proposed an optimized method Balance-
Yifeng Huang, Zhirong Tang, Kaixiong Su
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