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about
Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.Learning URI Selection Criteria to Improve the Crawling of Linked Open DataA Supervised Learning Approach for the Extraction of Sources and Targets from German TextPredicting default and non-default aspectual coding: Impact and density of information features
P4510
Prodepth: predict residue depth by support vector regression approach from protein sequences onlyDistinguishing protein-coding from non-coding RNAs through support vector machines.Sequence information for the splicing of human pre-mRNA identified by support vector machine classificationPredicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositionsmiTarget: microRNA target gene prediction using a support vector machineApplying Support Vector Machines for Gene Ontology based gene function predictionPreBIND and Textomy--mining the biomedical literature for protein-protein interactions using a support vector machineProfiled support vector machines for antisense oligonucleotide efficacy predictionThe SBASE domain sequence resource, release 12: prediction of protein domain-architecture using support vector machinesSupport vector machines for predicting protein structural classCharacterization of digital medical images utilizing support vector machinesMulticlass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection.Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs)GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.Better prediction of protein contact number using a support vector regression analysis of amino acid sequence.Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguationProtein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines.A sensitive, support-vector-machine method for the detection of horizontal gene transfers in viral, archaeal and bacterial genomes.Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding schemeClassification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.Gene selection algorithms for microarray data based on least squares support vector machine.Explaining Support Vector Machines: A Color Based NomogramIdentification of DNA-binding proteins using support vector machines and evolutionary profilesLarge-scale ligand-based predictive modelling using support vector machinesKnowledge-based analysis of microarray gene expression data by using support vector machinesOvercome support vector machine diagnosis overfittingSupport vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical librariesPredicting Caco-2 permeability using support vector machine and chemistry development kitRecognition and classification of histones using support vector machineSupport vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity searchPredicting full thickness skin sensitization using a support vector machinePredicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoproteinPrediction of cell penetrating peptides by support vector machinesA two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligandsUsing support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairmentSystematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysisPrediction of overall in vitro microsomal stability of drug candidates based on molecular modeling and support vector machines. Case study of novel arylpiperazines derivativesWavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI ScanningSupport vector machine with hypergraph-based pairwise constraintsSupport Vector Machine with Ensemble Tree Kernel for Relation Extraction
P921
Q21142748-513163E3-6576-4DD0-B413-9F4046799762Q21145271-44B3D87C-B081-4296-B685-EFA29DAFB5C0Q24561544-12E73C35-47A0-450C-A905-7FAA2ACC1BECQ24645090-D0F5821B-30E9-48A5-92C7-73F671C90676Q24670005-73728835-568F-4FAA-B34C-DDA7CD4A054AQ24792108-349D4E12-C78A-4B04-A7C0-CF568ABB9E40Q24792142-5B5E7DCF-96B3-450D-BB46-87355DE62C0CQ24792682-34EE6DC2-0F8B-4077-A391-93725476A13BQ24796953-6DD5BDA4-D2E1-40E1-8B1E-3F85148A9B30Q24801736-30E59E3C-135F-45DC-A83A-396D55ED19D8Q24806334-29996521-D4DB-4078-A504-01EDE37AE549Q24811720-90C398C0-9163-46B9-9A0D-29A7F9912371Q24811842-BFAF71FE-30B6-4875-B535-8F380FEF83C0Q24813500-5D8C1E36-6C7A-4904-B405-71823F4681E7Q24813700-3C119B97-7D68-4FE8-AA01-0971BA39F3FBQ24814876-13F40E14-6C60-4B40-BB71-1CD56322CC47Q24815173-A7CFCADD-49FF-4835-925C-82F91B5ABA0DQ24816340-C7C51A3C-A639-48CF-BDAB-6F6433BA81D2Q25255642-F8B24E33-CD6A-4089-B154-6FF0DACA72FBQ25256879-8DC7D140-5E18-4ABC-8E23-6F69450E0BE1Q25257168-937382DF-68DD-46C8-BB4C-59291901D49CQ27320338-5740D5E4-C2A2-4D64-A058-C9989337E316Q27496518-4814FF8C-D556-4E06-A282-E068F3970664Q27902338-3C0953F0-C9A1-45C5-BB40-925BF11D4FDCQ27939386-CAC45378-B2F5-4396-BE2F-AEEAEED07583Q28082900-13C72CBD-CE1B-413E-A929-88ADDB7AC29FQ28242361-75D35465-9167-423D-AAE4-5F0AB8938997Q28262353-BF499D18-9D08-4FA6-A2D3-7D8AAB2D0A9CQ28296699-21BCF0AB-C363-457B-A3D4-34186EC46488Q28302185-30A5D3D2-AA98-452B-8003-F003B5335A38Q28396621-62BB0748-511D-4058-A962-ACA42DEE872BQ28477346-FF72F6D4-0D26-4306-A511-98D6EA586ABAQ28479013-B575BFA9-B3C3-41B3-A191-38B26B91A15CQ28480436-E089EA1E-825C-4C96-8677-783C084BB719Q28481248-4CBF5785-077D-4D17-9176-9E3F80BA84AFQ28543875-F05899C6-0BD3-408C-A583-200F8EEFC680Q28545227-F92CD10C-E768-4455-B674-54281B13166FQ28597242-834802B1-56B9-46EB-8E62-DE3EFFA87A63Q28598382-58FA3180-38FA-4C65-9B91-03CA86497A56Q28600942-26447617-6049-4328-82A2-3685EA575AEC
P921
description
conjunt de mètodes per a l'aprenentatge estadístic supervisat
@ca
ensemble de techniques d'apprentissage supervisé
@fr
insieme di modelli di apprendimento supervisionato
@it
kumpulan metode-metode untuk supervised statistical learning
@id
mathematisches Verfahren der Mustererkennung
@de
set of methods for supervised statistical learning
@en
набір методів для статистичного навчання з учителем
@uk
用於監督統計學習的一套方法
@zh
name
Atraminių vektorių klasifikatorius
@lt
Destek vektör makinesi
@tr
Euskarri bektoredun makina
@eu
Maszyna wektorów nośnych
@pl
Metoda podpornih vektorjev
@sl
Máy vectơ hỗ trợ
@vi
Support Vector Machine
@de
Support vector machine
@nl
Support vector machines
@cs
Tugivektor-masin
@et
type
label
Atraminių vektorių klasifikatorius
@lt
Destek vektör makinesi
@tr
Euskarri bektoredun makina
@eu
Maszyna wektorów nośnych
@pl
Metoda podpornih vektorjev
@sl
Máy vectơ hỗ trợ
@vi
Support Vector Machine
@de
Support vector machine
@nl
Support vector machines
@cs
Tugivektor-masin
@et
altLabel
Maquinas de vectores de soporte
@es
SVM
@de
SVM
@en
SVM
@eo
SVM
@fr
SVM
@id
SVM
@it
SVM
@ro
SVM
@uk
machines à vecteurs de support
@fr
prefLabel
Atraminių vektorių klasifikatorius
@lt
Destek vektör makinesi
@tr
Euskarri bektoredun makina
@eu
Maszyna wektorów nośnych
@pl
Metoda podpornih vektorjev
@sl
Máy vectơ hỗ trợ
@vi
Support Vector Machine
@de
Support vector machine
@nl
Support vector machines
@cs
Tugivektor-masin
@et
P227
P244
P2581
P268
P486
P6366
P646
P227
P244
sh2008009003
P2581
P268
P3417
Support-Vector-Machines
P3553
P486
P571
1963-01-01T00:00:00Z
P6366
P646
P672
G17.035.250.500.500.500
L01.224.050.375.530.500.500