Results 31 to 40 of about 1,421,625 (273)
Meta-Ensemble Parameter Learning
, 2022 Ensemble of machine learning models yields improved performance as well as robustness. However, their memory requirements and inference costs can be prohibitively high. Knowledge distillation is an approach that allows a single model to efficiently capture the approximate performance of an ensemble while showing poor scalability as demand for re ...
Fei, Zhengcong +4 more
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The use of Bayesian network in analysis of urban intersection crashes in China
Transport, 2015 Traffic fatalities and injuries on urban roads especially at urban intersections constitute a growing problem in China. This study aims at researching urban intersection crashes in China and drawing conclusions by using hierarchical structured data with ...
Jinbao Zhao, Wei Deng
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Deep Learning Based Cognitive Radio Modulation Parameter Estimation
IEEE Access, 2023 Automatic Modulation Classification (AMC) is a critical issue in electromagnetic spatial perception. Currently traditional recognition techniques are difficult to adapt to complex signal situations.
Wenxuan Ma, Zhuoran Cai
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Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition
Sensors, 2019 To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a ...
Xia Zhang +4 more
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Bayesian Network Parameter Learning Method on Small Samples [PDF]
Jisuanji gongcheng, 2016 Maximum likelihood estimation is a classical and effective method for Bayesian network parameter learning on large samples,but it is not consistent when learning on small sample with little expertise.To address the issue,a novel method called TL-WMLE is ...
LI Zida,LIAO Shizhong
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Learn-Able Parameter Guided Activation Functions [PDF]
, 2020 14 pages, 9 ...
Balaji, S. +2 more
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A machine learning approach to Bayesian parameter estimation
npj Quantum Information, 2021 Bayesian estimation is a powerful theoretical paradigm for the operation of the approach to parameter estimation. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted
Samuel Nolan +2 more
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Learning Through Deterministic Assignment of Hidden Parameters [PDF]
IEEE Transactions on Cybernetics, 2020 Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how hidden predictors are linearly ...
Jian Fang, Shaobo Lin, Zongben Xu
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Bayesian Analysis of Bubbles in Asset Prices
Econometrics, 2017 We develop a new model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a
Andras Fulop, Jun Yu
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Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations
Mathematics, 2023 Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II ...
Yahia Abdel-Aty +2 more
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