Predicting sample size required for classification performance.
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PredicT-ML: a tool for automating machine learning model building with big clinical data.Determination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.Gut Microbiota Dysbiosis as Risk and Premorbid Factors of IBD and IBS Along the Childhood-Adulthood Transition.A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regionsActive learning for clinical text classification: is it better than random sampling?Use of SNP genotypes to identify carriers of harmful recessive mutations in cattle populations.Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine LearningMedical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset SessionThe role of machine learning in neuroimaging for drug discovery and development.Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.Automatic sleep staging using ear-EEG.Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.Longitudinal Changes in Audiometric Phenotypes of Age-Related Hearing Loss.Vital signs as predictors for aggression in hospital patients (VAPA).Feasibility study of individualized optimal positioning selection for left-sided whole breast radiotherapy: DIBH or prone.Novel Word Learning in Bilingual and Monolingual Infants: Evidence for a Bilingual Advantage.Exploiting and assessing multi-source data for supervised biomedical named entity recognition.Raman spectroscopy-based identification of toxoid vaccine productsWidespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy
P2860
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P2860
Predicting sample size required for classification performance.
description
2012 nî lūn-bûn
@nan
2012 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Predicting sample size required for classification performance.
@ast
Predicting sample size required for classification performance.
@en
Predicting sample size required for classification performance.
@nl
type
label
Predicting sample size required for classification performance.
@ast
Predicting sample size required for classification performance.
@en
Predicting sample size required for classification performance.
@nl
prefLabel
Predicting sample size required for classification performance.
@ast
Predicting sample size required for classification performance.
@en
Predicting sample size required for classification performance.
@nl
P2093
P2860
P356
P1476
Predicting sample size required for classification performance.
@en
P2093
Long H Ngo
Qing Zeng-Treitler
Rosa L Figueroa
Sasikiran Kandula
P2860
P2888
P356
10.1186/1472-6947-12-8
P577
2012-02-15T00:00:00Z
P5875
P6179
1009732326