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Kernel perceptronRadial basis function networkPolynomial kernelKernel embedding of distributionsGeneral regression neural networkProbabilistic neural networkKernel density estimationBackpropagationRelevance vector machineGradient boostingKernel regressionManifold regularizationNeural tangent kernelConditional random fieldFeature learningRestricted boltzmann machineSoftmax functionLogsumexpDirichlet kernelFisher kernelPerceptronKernelRpropDelta ruleGaussian functionGaussian random fieldCover's theoremRecursive least squares filterRegularized least squaresRecursive bayesian estimationGeneralized hebbian algorithmPoisson kernelBest linear unbiased predictionSequential minimal optimizationQr algorithmArnoldi iterationMarkov kernelDeep reinforcement learningKernel smootherTransition kernelBayesian vector autoregressionDirichlet convolutionRastrigin functionHierarchical dirichlet processPositive-definite kernelK-svdStatistical learning theoryEmpirical orthogonal functionsLinear predictor functionFredholm integral equationActivation functionGraph neural networkM-theoryBiconjugate gradient methodNested sampling algorithmEnsemble kalman filterHyperparameterLearning ratePrincipal component regressionBayesian structural time seriesFeatureRandom forestGaussian eliminationInverse iterationRademacher complexityReproducing kernel hilbert spaceRepresenter theoremInverse quadratic interpolationLinear regressionGradient descentLarge margin nearest neighborRecurrent neural networkGaussian blurBilinear interpolationPoisson regressionExtended kalman filterApproximate bayesian computationSupervised learningSliding dftHilbert transformPlatt scalingFeature hashingSobel operatorFinite element methodBackpropagation through timeBeta functionStochastic gradient descentOrdinary least squaresCovariance functionSparse pcaOptimal estimationProbabilistic classificationKuwahara filterBerlekamp's algorithmKrigingMargin classifierBelief propagationLocal regressionSimplex algorithmTotal variation denoising