Shape Quantization and Recognition with Randomized Trees
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MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targetsFinding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert AnnotationsSupervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury.Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.Vehicle Maneuver Detection with Accelerometer-Based Classification.CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer.Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitroExplaining the disability paradox: a cross-sectional analysis of the Swiss general population.A hybrid likelihood model for sequence-based disease association studies.Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annotation of somatic mutations (CHASM).Genome-wide prediction of three important traits in bread wheat.Energy-Efficient Neuromorphic Classifiers.Human Detection Using Random Color Similarity Feature and Random Ferns ClassifierSequence analysis of 515 kinase genes in chronic lymphocytic leukemia.Reinforcement Learning Trees.CRAVAT: cancer-related analysis of variants toolkit.Evaluating the evaluation of cancer driver genes.A computational model for visual selection.Human Pose Estimation from Monocular Images: A Comprehensive Survey.Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.Biview learning for human posture segmentation from 3D points cloud.Human-interpretable feature pattern classification system using learning classifier systems.MR volumetric assessment of endolymphatic hydrops.AncesTrees: ancestry estimation with randomized decision trees.Sequential Feature Selection and Inference using Multivariate Random Forests.Detecting changes between real-world objects using spatiochromatic filters.Learning real-world stimuli in a neural network with spike-driven synaptic dynamics.Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks.Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification.Source-enhanced coalescence of trees in a random forest.Attractor networks for shape recognition.Learning Optical Flow Propagation Strategies Using Random Forests for Fast Segmentation in Dynamic 2D & 3D EchocardiographyRandom Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D EchocardiographyEfficient Learning of Pre-attentive Steering in a Driving School FrameworkMulti-view object instance recognition in an industrial contextEnhanced flyby science with onboard computer vision: Tracking and surface feature detection at small bodies
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P2860
Shape Quantization and Recognition with Randomized Trees
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована в жовтні 1997
@uk
name
Shape Quantization and Recognition with Randomized Trees
@en
Shape Quantization and Recognition with Randomized Trees
@nl
type
label
Shape Quantization and Recognition with Randomized Trees
@en
Shape Quantization and Recognition with Randomized Trees
@nl
prefLabel
Shape Quantization and Recognition with Randomized Trees
@en
Shape Quantization and Recognition with Randomized Trees
@nl
P2860
P1433
P1476
Shape Quantization and Recognition with Randomized Trees
@en
P2093
Donald Geman
P2860
P304
P356
10.1162/NECO.1997.9.7.1545
P577
1997-10-01T00:00:00Z