Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.
about
A mathematical model for interpretable clinical decision support with applications in gynecology.Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models.Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data.Genome-wide selection by mixed model ridge regression and extensions based on geostatistical modelsMultiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.A comparison of two spelling Brain-Computer Interfaces based on visual P3 and SSVEP in Locked-In SyndromeIdentification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profilesA RLS-SVM Aided Fusion Methodology for INS during GPS Outages.Predicting Interrelated Alzheimer's Disease Outcomes via New Self-Learned Structured Low-Rank Model.A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM.Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers.Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation.A 65‑gene signature for prognostic prediction in colon adenocarcinoma.Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis.Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification.Kernel least-squares models using updates of the pseudoinverse.SMO algorithm for least-squares SVM formulations.Sensitivity and specificity can change in opposite directions when new predictive markers are added to risk models.Multivariate Calibration of Near Infrared Spectroscopy in the Presence of Light Scattering Effect: A Comparative StudyA Novel Sparse Least Squares Support Vector MachinesA New Method for Solving Supervised Data Classification ProblemsResearch on a Novel Kernel Based Grey Prediction Model and Its Applications
P2860
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P2860
Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.
description
2002 nî lūn-bûn
@nan
2002年の論文
@ja
2002年学术文章
@wuu
2002年学术文章
@zh
2002年学术文章
@zh-cn
2002年学术文章
@zh-hans
2002年学术文章
@zh-my
2002年学术文章
@zh-sg
2002年學術文章
@yue
2002年學術文章
@zh-hant
name
Bayesian framework for least-s ...... Fisher discriminant analysis.
@en
Bayesian framework for least-s ...... Fisher discriminant analysis.
@nl
type
label
Bayesian framework for least-s ...... Fisher discriminant analysis.
@en
Bayesian framework for least-s ...... Fisher discriminant analysis.
@nl
prefLabel
Bayesian framework for least-s ...... Fisher discriminant analysis.
@en
Bayesian framework for least-s ...... Fisher discriminant analysis.
@nl
P2093
P2860
P1433
P1476
Bayesian framework for least-s ...... Fisher discriminant analysis.
@en
P2093
Lambrechts A
Lanckriet G
Suykens JA
Van Gestel T
Vandewalle J
P2860
P304
P356
10.1162/089976602753633411
P577
2002-05-01T00:00:00Z