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kernel method


'kernel method' can also refer to...

kernel method

kernel method

Kernel methods for predicting protein–protein interactions

Seismic tomography, adjoint methods, time reversal and banana-doughnut kernels

Sparse kernel methods for high-dimensional survival data

Sensitivity kernels for viscoelastic loading based on adjoint methods

Kernel methods for large-scale genomic data analysis

Absorbing Kernels to Study Resonances in the Generator Coordinate Method

An efficient graph kernel method for non-coding RNA functional prediction

An efficient Born normal mode method to compute sensitivity kernels and synthetic seismograms in the Earth

A Video Semantic Analysis Method Based on Kernel Discriminative Sparse Representation and Weighted KNN

Finite-frequency sensitivity kernels for global seismic wave propagation based upon adjoint methods

Finite-frequency kernels for wave propagation in porous media based upon adjoint methods

The ZH ratio method for long-period seismic data: sensitivity kernels and observational techniques

Recognition of multiple patterns in unaligned sets of sequences: comparison of kernel clustering method with other methods

An Artificial Neural Network Approach for Glomerular Activity Pattern Prediction Using the Graph Kernel Method and the Gaussian Mixture Functions

A second-order accurate numerical method for a semilinear integro-differential equation with a weakly singular kernel

Asymptotic expansion and Filon-type methods for a Volterra integral equation with a highly oscillatory kernel

Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data

 

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A method for the estimation of probability density functions. Suppose X is a continuous random variable with unknown probability density function f. A random sample of observations of X is taken. If the sample values are denoted by x1, x2,…, xn, the estimate of f is given by where K is a kernel function and the constant A is chosen so that The observation xj may be regarded as being spread out between xja and xj+b (usually with a=b). The result is that the naive estimate of f as being a function capable of taking values only at x1, x2,…, xn, is replaced by a continuous function having peaks where the data are densest. Examples of kernel functions are the Gaussian kernel, and the Epanechikov kernel,, The constant h is the window width or bandwidth. The choice of h is critical: small values may retain the spikes of the naive estimate, and large values may oversmooth so that important aspects of f are lost.,

Kernel method. In this case a sample of twenty observations have been generated randomly from a chi-squared distribution with twenty degrees of freedom and a Gaussian kernel with h=3 has been used to generate the kernel density estimate.

Subjects: Probability and Statistics.


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