Results 31 to 40 of about 332,886 (299)
Frequentist parameter estimation with supervised learning [PDF]
AVS Quantum Science, 2021 Recently, there has been a great deal of interest surrounding the calibration of quantum sensors using machine learning techniques. This work explores the use of regression to infer a machine-learned point estimate of an unknown parameter. Although the analysis is necessarily frequentist—relying on repeated estimates to build up statistics—the authors ...
Samuel P. Nolan +2 more
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Active learning BSM parameter spaces
European Physical Journal C: Particles and Fields, 2023 Active learning (AL) has interesting features for parameter scans of new models. We show on a variety of models that AL scans bring large efficiency gains to the traditionally tedious work of finding boundaries for BSM models.
Mark D. Goodsell, Ari Joury
doaj +1 more source
Constrained Parameter Inference as a Principle for Learning
Trans. Mach. Learn. Res., 2022 18 p.
Ahmad, N. +2 more
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Construction and Reasoning Approach of Belief Rule-Base for Classification Base on Decision Tree
IEEE Access, 2020 The classical belief rule-based (BRB) systems are usually constructed by arranging and combining referential values of antecedent attributes or by setting special fixed values, which can lead to overly large size of BRB systems in complex problems.
Yanggeng Fu +4 more
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Mixed‐frequency predictive regressions with parameter learning [PDF]
, 2023 We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically,
Markus Leippold +3 more
core +1 more source
Research on Intelligent Maneuvering Decision-Making in Close Air Combat Based on Deep Q Network [PDF]
Hangkong bingqi, 2023 Aiming at the problem of UCAV maneuvering decision-making in close air combat, the design of reinforcement learning reward function and the selection of hyper-parameters are studied based on the framework of deep Q network algorithm.
Zhang Tingyu, Sun Mingwei, Wang Yongshuai, Chen Zengqiang
doaj +1 more source
Predicting Parameters in Deep Learning
CoRR, 2013 We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all.
Denil, M +4 more
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Parameter Learning of Bayesian Network with Multiplicative Synergistic Constraints
, 2022 Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available.
Zhiming Hu, Yu Zhang
core +1 more source
Learning Populations of Parameters
, 2017 Consider the following estimation problem: there are $n$ entities, each with an unknown parameter $p_i \in [0,1]$, and we observe $n$ independent random variables, $X_1,\ldots,X_n$, with $X_i \sim $ Binomial$(t, p_i)$. How accurately can one recover the "histogram" (i.e. cumulative density function) of the $p_i$'s?
Kevin Tian, Weihao Kong, Gregory Valiant
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Effects of Learning Parameters on Learning Procedure and Performance of a BPNN [PDF]
Neural Networks, 1997 We examined the effects of changing learning parameters on the learning procedure and performance of back-propagation neural networks used to pick seismic arrivals. The results show that such change mainly affects the speed of convergence of the learning procedures, and does not affect the BPNN structure and its overall performance.
Hengchang Dai, Colin MacBeth
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