N%C3%A1hodn%C3%BD_lesRandom_ForestRandom_forestRandom_forestOtsustusmets%D8%AC%D9%86%DA%AF%D9%84_%D8%AA%D8%B5%D8%A7%D8%AF%D9%81%DB%8CFor%C3%AAt_d%27arbres_d%C3%A9cisionnelsRandom_ForestRandom_forestForesta_casuale%E3%83%A9%E3%83%B3%E3%83%80%E3%83%A0%E3%83%95%E3%82%A9%E3%83%AC%E3%82%B9%E3%83%88%EB%9E%9C%EB%8D%A4_%ED%8F%AC%EB%A0%88%EC%8A%A4%ED%8A%B8Las_losowyRandom_forestRandom_forestRastgele_ormanRandom_forestQ245748%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97
about
sameAs
P800
Random ForestsApplication of the random forest method in studies of local lymph node assay based skin sensitization dataRandom KNN feature selection - a fast and stable alternative to Random ForestsPrediction of the metabolic syndrome status based on dietary and genetic parameters, using Random ForestScreening large-scale association study data: exploiting interactions using random forestsGene selection and classification of microarray data using random forest.Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regressionUsing Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.GPURFSCREEN: a GPU based virtual screening tool using random forest classifierPrediction of protein-protein interaction sites in sequences and 3D structures by random forestsPrediction of protein-protein interaction sites by random forest algorithm with mRMR and IFSPrediction of protein cleavage site with feature selection by random forestAre Random Forests Truly the Best Classifiers?Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?Analysis and prediction of highly effective antiviral peptides based on random forestsQSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forestA Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity PredictionRandom Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin GenerationDiscovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and DockingRAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing SystemsDisaggregating census data for population mapping using random forests with remotely-sensed and ancillary dataPrediction of G Protein-Coupled Receptors with SVM-Prot Features and Random ForestCURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forestsRandom forest automated supervised classification of Hipparcos periodic variable starsMapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS, Landsat 7 ETM+, SRTM and multiple Random ForestsOptimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.Improving the chances of successful protein structure determination with a random forest classifierInsight into Best Variables for COPD Case Identification: A Random Forests AnalysisRandom forest-based protein model quality assessment (RFMQA) using structural features and potential energy termsImproving protein fold recognition by random forest.Predicting host tropism of influenza A virus proteins using random forest.Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest.DNA-Prot: identification of DNA binding proteins from protein sequence information using random forest.Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression.Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins.regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution.RSARF: prediction of residue solvent accessibility from protein sequence using random forest method.Predicting residue-residue contacts using random forest models.A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification.
P921
Q22673963-6849c447-4f5c-de47-4933-caa0bf80de75Q23921328-85A4D5A7-1280-4686-BC96-7A3FC8527D99Q23921329-06990ED5-7B8A-40A4-B9EA-99D8A0C8EE5AQ24651436-B121FBCA-49B9-479C-9B3A-8CAC53531D50Q24809535-60CBAFBA-65D3-42C4-9E52-21F45C07FFCBQ25255911-F0A27694-634D-4D2B-AA14-4E240B910B0AQ27308815-B89BA01E-4AD1-4C7E-A350-3D24834D86DBQ27333636-04EEA92E-877F-4533-A047-701FD12B98F5Q27902319-9ED2652C-E3BE-42B6-9404-705B171E4C93Q28474551-09bd887b-4e1f-ccde-4ed2-026e155199b4Q28482815-C85FFD96-3B6E-4E36-A436-0861E8ED5F33Q28483826-1C8F2C3E-1DD5-4036-BD58-4B6CC5EDA075Q28529572-61b0f65a-4e84-2462-b03c-c24a61dfcf57Q28529587-28cce667-4b2b-c7e1-bdf2-ecc24cbcb844Q28535105-978E183F-E68B-4CF7-A026-D43D9DF8A1FEQ28544930-F6C89B81-CEE1-4449-B82F-5A3FF674F01EQ28551596-7527410E-D8CA-4F13-B6D2-9DB28ED4D6C7Q28552035-1f566476-4f11-84b2-df4b-dc68e570f49aQ28553112-1D3039DD-C882-4160-8A2D-0B684216A540Q28601791-13A26DB4-91C3-427A-8C09-E794B11285A0Q28649810-2CCF5BD3-EE2D-477E-A61B-148BEF385593Q28830857-0E8DE669-7D06-40CD-ABD6-325FAF1006E7Q29248564-E856C5D4-1345-4AE8-B868-C349FD574DC9Q29304215-D2A940AD-C968-4EA6-A2D1-C4B09DAF6ACDQ30048746-740DEA2B-2A0F-4CEF-9851-D3F4073F50E4Q30301257-497A4FED-94E7-4EBB-A729-92B1246F6CE0Q30359799-40629F63-4E62-48F8-81BE-251C422DAFACQ30361262-6C892171-A20C-40CA-989A-414A066C43E9Q30366792-08818857-C428-467F-B3B9-4DC1A709EE38Q30368194-82D23D83-14AB-44B8-A877-46217222129BQ30369947-E4F89B2F-3650-42C2-A609-62B40F84509BQ30373871-33165628-538F-426A-A6AD-24F8670D9C1FQ30376355-313A9448-F8E2-40C3-95E1-7579B3EE4734Q30379031-E5722369-C6A3-4847-9FE4-151E9555B7D6Q30390235-1FF720D8-7848-4305-B460-53989DF96039Q30400560-58727E1D-6E91-444C-997F-7016F747C145Q30400919-DE49A022-BA5C-4B87-A463-2A582C38210CQ30407107-6A0C8928-7958-4026-9724-7762C7A7E2F4Q30408474-38FB3ADE-5CA0-4762-9626-4504F63D83A0Q30482925-69D7899B-1B2A-42B9-868D-F922EA2C10DE
P921
description
Algorithmus aus dem Machinellen Lernen
@de
algorithme d'apprentissage statistique
@fr
algoritmo estatístico que é usado para agrupar pontos de dados em grupos funcionais
@pt
statistical algorithm that is used to cluster points of data in functional groups
@en
алгоритм машинного обучения
@ru
name
Foresta casuale
@it
Náhodný les
@cs
Otsustusmets
@et
Random Forest
@de
Random Forest
@gl
Random Forest
@id
Random forest
@ja
Random forest
@pl
Random forest
@ru
Random forest
@uk
type
label
Foresta casuale
@it
Náhodný les
@cs
Otsustusmets
@et
Random Forest
@de
Random Forest
@gl
Random Forest
@id
Random forest
@ja
Random forest
@pl
Random forest
@ru
Random forest
@uk
altLabel
Random forest
@it
Zufallswald
@de
floresta aleatória
@pt
forêt aléatoire
@fr
forêts aléatoires
@fr
pădure aleatorie
@ro
random forests
@en
random forests
@fr
randomized trees
@en
tilfældig skov
@da
prefLabel
Foresta casuale
@it
Náhodný les
@cs
Otsustusmets
@et
Random Forest
@de
Random Forest
@gl
Random Forest
@id
Random forest
@ja
Random forest
@pl
Random forest
@ru
Random forest
@uk
P6366
P646
P1482
P31
P3417
Random-Forests-Algorithm
P571
2001-01-01T00:00:00Z