Results 41 to 50 of about 332,886 (299)

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
doaj   +1 more source

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
doaj   +1 more source

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, Youchao Sun, Zhifan Qiu, Junping Bao, Yanjun Zhang   +4 more
doaj   +1 more source

Learning to Learn Domain-invariant Parameters for Domain Generalization

CoRR, 2022
Submitted to ICASSP ...
Feng Hou, Yao Zhang 0010, Yang Liu 0250, Jin Yuan, Cheng Zhong, Yang Zhang 0002, Zhongchao Shi, Jianping Fan 0007, Zhiqiang He 0002   +8 more
openaire   +2 more sources

Irrelevance and parameter learning in Bayesian networks

, 1996
It is possible to learn the parameters of a given Bayesian network structure from data because those parameters influence the probability of observing the data.
Zhang, Nevin Lianwen
core   +2 more sources

Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors

, 2016
In this work, we investigated the potential of a recently proposed parameter learning algorithm for Conditional Random Fields (CRFs). Parameters of a pairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem.
Karamitsou, Venetia, Mauricio Reyes, Meier, Raphael, Wiest, Roland, Reyes, Mauricio, Raphael Meier, Simon Habegger, Venetia Karamitsou, Habegger, Simon, Roland Wiest   +9 more
core   +1 more source

Manifold learning for parameter reduction

Journal of Computational Physics, 2019
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that reduces complexity and facilitates exploration of behavioral aspects of otherwise intractable models.
Holiday, Alexander, Kooshkbaghi, Mahdi, Bello-Rivas, Juan M., William Gear, C., Zagaris, Antonios, Kevrekidis, Ioannis G.   +5 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
doaj   +1 more source

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, Augusto Smerzi, Luca Pezzè   +2 more
doaj   +1 more source

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 ...
openaire   +3 more sources