An empirical approach to model selection through validation for censored survival data.
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Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression.Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair.Diagnosis of early stage nasopharyngeal carcinoma using ultraviolet autofluorescence excitation-emission matrix spectroscopy and parallel factor analysis.Sexual Assault Victimization and Mental Health Treatment, Suicide Attempts, and Career Outcomes Among Women in the US Army.
P2860
An empirical approach to model selection through validation for censored survival data.
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2011 nî lūn-bûn
@nan
2011 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
An empirical approach to model selection through validation for censored survival data.
@ast
An empirical approach to model selection through validation for censored survival data.
@en
type
label
An empirical approach to model selection through validation for censored survival data.
@ast
An empirical approach to model selection through validation for censored survival data.
@en
prefLabel
An empirical approach to model selection through validation for censored survival data.
@ast
An empirical approach to model selection through validation for censored survival data.
@en
P2093
P1476
An empirical approach to model selection through validation for censored survival data
@en
P2093
Changhong Yu
Ickwon Choi
Michael W Kattan
P304
P356
10.1016/J.JBI.2011.02.005
P50
P577
2011-02-16T00:00:00Z