Results 41 to 50 of about 8,034,918 (294)
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
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Learning to Learn Domain-invariant Parameters for Domain Generalization
CoRR, 2022 Submitted to ICASSP ...
Feng Hou +8 more
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Parameter learning in production economies
Journal of Monetary Economics, 2018 We examine how parameter learning amplifies the impact of macroeconomic shocks on equity prices and quantities in a standard production economy where a representative agent has Epstein-Zin preferences. An investor observes technology shocks which follow a regime-switching process but does not know the underlying model parameters governing the short ...
Mykola Babiak, Roman Kozhan
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PbP2: Automatic Configuration of a Portfolio-based Multi-Planner [PDF]
We present PbP2, an automated system that generates efficient domain-specific multi planners from a portfolio of domain-independent planning techniques by (i) computing some sets of macro-actions for every planner in the portfolio, (ii) optimizing the ...
Vallati, Mauro +2 more
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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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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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Learning Bound for Parameter Transfer Learning
CoRR, 2016 We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping,and thereby derive a learning bound for ...
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An Adaptive Linear Programming Algorithm with Parameter Learning
AlgorithmsWhen dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems.
Lin Guo +4 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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Seismic Signal Classification Using Perceptron With Different Learning Rules
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Perceptron is adopted to classify the Ricker wavelets and to detect the seismic anomaly in a seismogram. Three learning rules are used in the training of perceptron to solve the decision boundary. The optimal learning-rate parameter is derived. The lower
Kou-Yuan Huang +2 more
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