Local-learning-based feature selection for high-dimensional data analysis.
about
Genomic prediction based on data from three layer lines using non-linear regression models.Feature weight estimation for gene selection: a local hyperlinear learning approach.Computational approach for deriving cancer progression roadmaps from static sample data.Advanced computational algorithms for microbial community analysis using massive 16S rRNA sequence dataDerivation of cancer diagnostic and prognostic signatures from gene expression data.False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines.Cancer progression modeling using static sample dataDetecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers.MORPHOLOGICAL SIGNATURES AND GENOMIC CORRELATES IN GLIOBLASTOMAMulti-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancerOptimal combination of feature selection and classification via local hyperplane based learning strategy.Molecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis.AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study.A candidate molecular biomarker panel for the detection of bladder cancer.Bladder cancer detection and monitoring: assessment of urine- and blood-based marker testsDistinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognitionMULTIPLEX URINARY TESTS FOR BLADDER CANCER DIAGNOSIS.Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics.Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification.Identity Recognition Using Biological Electroencephalogram Sensors
P2860
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P2860
Local-learning-based feature selection for high-dimensional data analysis.
description
2010 nî lūn-bûn
@nan
2010 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Local-learning-based feature selection for high-dimensional data analysis.
@ast
Local-learning-based feature selection for high-dimensional data analysis.
@en
type
label
Local-learning-based feature selection for high-dimensional data analysis.
@ast
Local-learning-based feature selection for high-dimensional data analysis.
@en
prefLabel
Local-learning-based feature selection for high-dimensional data analysis.
@ast
Local-learning-based feature selection for high-dimensional data analysis.
@en
P2093
P2860
P356
P1476
Local-learning-based feature selection for high-dimensional data analysis.
@en
P2093
Sinisa Todorovic
Steve Goodison
P2860
P304
P356
10.1109/TPAMI.2009.190
P407
P577
2010-09-01T00:00:00Z