scholarly journals Learning Bayesian Network Parameters With Small Data Set: A Parameter Extension under Constraints Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 24979-24989
Author(s):  
Yongyan Hou ◽  
Enrang Zheng ◽  
Wenqiang Guo ◽  
Qinkun Xiao ◽  
Ziwei Xu
Keyword(s):  
Bayesian Network ◽  
Small Data ◽  
Data Set ◽  
Network Parameters ◽  
Small Data Set

Learning Bayesian Network Structure from Small Data Set

2009 ◽  
Vol 35 (8) ◽  
pp. 1063-1070 ◽  
Author(s):  
Shuang-Cheng WANG ◽  
Cui-Ping LENG ◽  
Xiao-Lin LI
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Small Data ◽  
Data Set ◽  
Bayesian Network Structure ◽  
Small Data Set

scholarly journals A Decomposition Algorithm for Learning Bayesian Networks Based on Scoring Function

2012 ◽  
Vol 2012 ◽  
pp. 1-17
Author(s):  
Mingmin Zhu ◽  
Sanyang Liu
Keyword(s):  
Bayesian Network ◽  
Scoring Function ◽  
Small Data ◽  
Hard Problem ◽  
High Complexity ◽  
Data Set ◽  
Small Data Set ◽  
Np Hard Problem ◽  
Moral Graph ◽  
Very High

Learning Bayesian network (BN) structure from data is a typical NP-hard problem. But almost existing algorithms have the very high complexity when the number of variables is large. In order to solve this problem(s), we present an algorithm that integrates with a decomposition-based approach and a scoring-function-based approach for learning BN structures. Firstly, the proposed algorithm decomposes the moral graph of BN into its maximal prime subgraphs. Then it orientates the local edges in each subgraph by the K2-scoring greedy searching. The last step is combining directed subgraphs to obtain final BN structure. The theoretical and experimental results show that our algorithm can efficiently and accurately identify complex network structures from small data set.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

Automatic Speech Recognition for Portuguese with Small Data Set

2021 ◽  
pp. 1-13
Author(s):  
Yapeng Wang ◽  
Ruize Jia ◽  
Chan Tong Lam ◽  
Ka Cheng Choi ◽  
Koon Kei Ng ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Small Data ◽  
Data Set ◽  
Small Data Set

scholarly journals Bayesian Network Model for Flood Forecasting Based on Atmospheric Ensemble Forecasts

2019 ◽  
Author(s):  
Leila Goodarzi ◽  
Mohammad Ebrahim Banihabib ◽  
Abbas Roozbahani ◽  
Jörg Dietrich
Keyword(s):  
Bayesian Network ◽  
Cumulus Parameterization ◽  
Small Data ◽  
Ensemble Forecasts ◽  
Validation Data ◽  
Data Set ◽  
Flood Peak ◽  
Absolute Relative Error ◽  
Cumulus Parameterization Schemes ◽  
Artificial Neural Network Ann

Abstract. The purpose of this study is to propose the Bayesian Network (BN) model to estimate flood peak from Atmospheric Ensemble Forecasts (AEFs). The Weather Research and Forecasting model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to forecast flood peak from AEFs. Mean Absolute Relative Error was calculated as 0.076 for validation data while it was calculated as 0.39 in artificial neural network (ANN) as a widely used model. It seems that BN is less sensitive to small data set, thus it is more suited for forecasting flood peak than ANN.

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