Search results for "Non-parametric"

showing 3 items of 13 documents

Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algo…

2019

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classify…

non-parametric classificationComputer science020209 energyHealth Toxicology and Mutagenesislcsh:Medicine02 engineering and technology010501 environmental sciencesengineering.material01 natural sciencesArticleDigital imageSoftwareArtificial Intelligence0202 electrical engineering electronic engineering information engineeringLearningTopological map0105 earth and related environmental sciencesLVQ algorithmLearning vector quantizationArtificial neural networkSOFM neural networkCompostbusiness.industryCompostinglcsh:RPublic Health Environmental and Occupational Health<i>LVQ</i> algorithmengineeringNeural Networks ComputerbusinessClassifier (UML)AlgorithmAlgorithmsSoftwareInternational Journal of Environmental Research and Public Health
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CALIBRATION OF LÉVY PROCESSES USING OPTIMAL CONTROL OF KOLMOGOROV EQUATIONS WITH PERIODIC BOUNDARY CONDITIONS

2018

We present an optimal control approach to the problem of model calibration for L\'evy processes based on a non parametric estimation procedure. The calibration problem is of considerable interest in mathematical finance and beyond. Calibration of L\'evy processes is particularly challenging as the jump distribution is given by an arbitrary L\'evy measure, which form a infinite dimensional space. In this work, we follow an approach which is related to the maximum likelihood theory of sieves. The sampling of the L\'evy process is modelled as independent observations of the stochastic process at some terminal time $T$. We use a generic spline discretization of the L\'evy jump measure and selec…

non-parametric maximum likelihood methodOptimization problemDiscretizationL ́evy processesoptimal control of PIDE010103 numerical & computational mathematics01 natural sciences93E10 (primary) 49K20 60G51 62G05 (secondary)010104 statistics & probabilitysymbols.namesakeConjugate gradient methodIMEX numerical methodQA1-939Applied mathematics0101 mathematicsMathematics - Optimization and ControlMathematicsKolmogorov-Fokker-Planck equationoptimal control of PIDE Kolmogorov-Fokker-Planck equation L ́evy processes non-parametric maximum likelihood method IMEX numerical method.SolverOptimal controlSpline (mathematics)Lévy processesModeling and SimulationLagrange multipliersymbolsAkaike information criterionMathematicsAnalysisMathematical Modelling and Analysis
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Acoustic, neural, and perceptual correlates of polyphonic timbre

2012

polyphonic timbremonophonic timbresointivärifMRInon-parametric statistical significance estimationhavaitseminenmusiikkipsykologiakognitiiviset prosessitfunctional magnetic resonance imagingacoustic featureshomofoniakorrelaatioaivokuoritoiminnallinen magneettikuvausperceptual dimensionstunteetakustiikkaaivotutkimuspolyfoniaaivot
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