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   Articles with "balanced loss" as a keyword



Linearly admissible estimators of the common mean parameter under balanced loss function

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recommendations! Published in 2017 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2013.786783

Abstract: ABSTRACT Admissibility of linear estimators of the common mean parameter is investigated in the context of a linear model under balanced loss function. Sufficient and necessary conditions for linear estimators to be admissible in classes… read more here.

Keywords: mean parameter; balanced loss; estimators common; common mean ... See more keywords

Posterior analysis of the compound Rayleigh distribution under balanced loss functions for censored data

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recommendations! Published in 2017 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2015.1019140

Abstract: ABSTRACT This paper develops Bayesian analysis in the context of progressively Type II censored data from the compound Rayleigh distribution. The maximum likelihood and Bayes estimates along with associated posterior risks are derived for reliability… read more here.

Keywords: rayleigh distribution; balanced loss; censored data; compound rayleigh ... See more keywords

Adaptive Spatial Location With Balanced Loss for Video Captioning

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recommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2020.3045735

Abstract: Many pioneering approaches have verified the effectiveness of utilizing the global temporal and local object information for video understanding tasks and have achieved significant progress. However, existing methods utilize object detectors to extract all objects… read more here.

Keywords: spatial location; adaptive spatial; balanced loss; video ... See more keywords

Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions

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recommendations! Published in 2024 at "Neurocomputing"

DOI: 10.48550/arxiv.2407.14381

Abstract: Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks. While Gradient Boosting Decision Trees (GBDT) models have proven highly effective for such tasks, their performance can be compromised when… read more here.

Keywords: imbalanced datasets; balanced loss; class balanced; class ... See more keywords