Search results for "nearest neighbours"

showing 5 items of 5 documents

Assessing Causality in normal and impaired short-term cardiovascular regulation via nonlinear prediction methods

2009

We investigated the ability of mutual nonlinear prediction methods to assess causal interactions in short-term cardiovascular variability during normal and impaired conditions. Directional interactions between heart period (RR interval of the ECG) and systolic arterial pressure (SAP) short-term variability series were quantified as the cross-predictability (CP) of one series given the other, and as the predictability improvement (PI) yielded by the inclusion of samples of one series into the prediction of the other series. Nonlinear prediction was performed through global approximation (GA), approximation with locally constant models (LA0) and approximation with locally linear models (LA1) …

Adultmedicine.medical_specialtySupine positionTime FactorsGeneral MathematicsRR intervalGlobal nonlinear predictionGeneral Physics and AstronomyNeurally-mediated syncopeBlood PressureK-nearest neighbours local nonlinear predictionCardiovascular SystemSyncopeCardiovascular Physiological PhenomenaPhysics and Astronomy (all)Engineering (all)Control theoryHeart RateNeurally mediated syncopeInternal medicinemedicinePressureHumansMathematics (all)Computer SimulationOut-of-sample predictionMathematicsModels StatisticalGeneral EngineeringLinear modelModels CardiovascularNonlinear granger causalityModels TheoreticalControl subjectsHeart rate and arterial pressure variabilityCausalityNonlinear predictionTerm (time)Case-Control StudiesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCardiologyAlgorithms
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Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences

2007

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be p…

Computer Aided DetectionSupport Vector MachineNeural NetworksK-Nearest Neighbours
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Massive Lesions Classification using Features based on Morphological Lesion Differences

2007

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensiti…

Neural Networks; K-Nearest Neighbours; Support Vector Machine; Computer Aided DiagnosisSupport Vector MachineSupportVector MachineNeural NetworksComputer Aided DiagnosisK-Nearest NeighboursNeural Networks K-Nearest Neighbours Support Vector Machine Computer Aided Diagnosis.Computer Aided Diagnosis.
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Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

2019

International audience; Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, k…

Statistics and Probabilityconstructionkernel smoothingPopulationSurvey samplingimputation01 natural sciences010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]0502 economics and businessStatisticsImputation (statistics)0101 mathematicseducationsurvey samplingfunctional data050205 econometrics Mathematicsconfidence bandsConsumption (economics)Estimationeducation.field_of_studymissing completely at randombusiness.industry05 social sciencesprincipal analysis by conditional estimationSampling (statistics)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]nearest neighboursKernel smoothervariance-estimationElectricityStatistics Probability and Uncertaintybusinessvariance approximation
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Robust estimation of mean electricity consumption curves by sampling for small areas in presence of missing values

2017

In this thesis, we address the problem of robust estimation of mean or total electricity consumption curves by sampling in a finite population for the entire population and for small areas. We are also interested in estimating mean curves by sampling in presence of partially missing trajectories.Indeed, many studies carried out in the French electricity company EDF, for marketing or power grid management purposes, are based on the analysis of mean or total electricity consumption curves at a fine time scale, for different groups of clients sharing some common characteristics.Because of privacy issues and financial costs, it is not possible to measure the electricity consumption curve of eac…

Linear mixed modelsSmall area estimationMissing dataRegression treesEstimation sur petits domaines[MATH.MATH-GM] Mathematics [math]/General Mathematics [math.GM]Estimateurs à noyauModèles linéaires mixtesRandom forestsBiais conditionnelsFunctional dataSurvey sampling[MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM]RobustesseDonnées fonctionnellesPlus proches voisinsForêts aléatoiresConditional biasKernel estimatorsNearest neighboursSondageDonnées manquantesRobustnessArbres de régression
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