Classifier-Based Pattern Selection Approach for Relation Instance Extraction

2018 ◽  
pp. 418-434
Author(s):  
Angrosh Mandya ◽  
Danushka Bollegala ◽  
Frans Coenen ◽  
Katie Atkinson
Keyword(s):  
Pattern Selection ◽  
Selection Approach

ON SUITABLE MODEL SELECTION APPROACH FOR ANALYSIS OF MENTAL HEALTH SERVICE USE DATA

2019 ◽  
Vol 43 (2) ◽  
pp. 212-222
Author(s):  
DULAL K. BHAUMIK ◽  
◽  
SUBHASH ARYAL ◽  
SCOTT HUR ◽  
◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Model Selection ◽  
Health Service ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use ◽  
Suitable Model ◽  
Model Selection Approach ◽  
Selection Approach

A throughput-aware joint vehicle route and access network selection approach based on SMDP

2020 ◽  
Vol 17 (5) ◽  
pp. 243-265 ◽  
Author(s):  
Jiandong Xie ◽  
Sa Xiao ◽  
Ying-Chang Liang ◽  
Li Wang ◽  
Jun Fang
Keyword(s):  
Access Network ◽  
Network Selection ◽  
Vehicle Route ◽  
Selection Approach ◽  
Access Network Selection

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Limited Information ◽  
Online Application ◽  
Stochastic Search Variable Selection ◽  
Selection Approach ◽  
Pain Ratings ◽  
Research Questions ◽  
Standard Techniques ◽  
Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

Research on Service Selection Approach Based on Composite Service Execution Information

2009 ◽  
Vol 31 (8) ◽  
pp. 1398-1411 ◽  
Author(s):  
Ming-Wei ZHANG ◽  
Wei-Jie WEI ◽  
Bin ZHANG ◽  
Xi-Zhe ZHANG ◽  
Zhi-Liang ZHU
Keyword(s):  
Service Selection ◽  
Composite Service ◽  
Service Execution ◽  
Selection Approach

scholarly journals Pattern selection in a ring of Kuramoto oscillators

2019 ◽  
Vol 78 ◽  
pp. 104868 ◽  
Author(s):  
Károly Dénes ◽  
Bulcsú Sándor ◽  
Zoltán Néda
Keyword(s):  
Pattern Selection ◽  
Kuramoto Oscillators
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