ROC-based utility function maximization for feature selection and classification with applications to high-dimensional protease data.
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Deep Learning in Label-free Cell ClassificationA comparative study of variable selection methods in the context of developing psychiatric screening instrumentsSurvival associated pathway identification with group Lp penalized global AUC maximization.Plasma phospholipids identify antecedent memory impairment in older adults.Efficient statistical tests to compare Youden index: accounting for contingency correlation.Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps.
P2860
ROC-based utility function maximization for feature selection and classification with applications to high-dimensional protease data.
description
2008 nî lūn-bûn
@nan
2008 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի մարտին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
ROC-based utility function max ...... igh-dimensional protease data.
@ast
ROC-based utility function max ...... igh-dimensional protease data.
@en
type
label
ROC-based utility function max ...... igh-dimensional protease data.
@ast
ROC-based utility function max ...... igh-dimensional protease data.
@en
prefLabel
ROC-based utility function max ...... igh-dimensional protease data.
@ast
ROC-based utility function max ...... igh-dimensional protease data.
@en
P2860
P1433
P1476
ROC-based utility function max ...... igh-dimensional protease data.
@en
P2093
P2860
P304
P356
10.1111/J.1541-0420.2008.01015.X
P407
P577
2008-03-24T00:00:00Z