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Non-Parametric Estimation - Pattern Recognition - Lecture Slides

Slides, Mehanics And Mechanical Engineering

Post: April 19th, 2013
Description
The key points are: Non-Parametric Estimation, Density Functions, Kernel-Density Estimate, Parzen Window, Unit Hypercube, Data Points Falling, Kind of Generalization, Erecting Bins, D-Dimensional Gaussian Density, Gaussian Kernel
The key points are: Non-Parametric Estimation, Density Functions, Kernel-Density Estimate, Parzen Window, Unit Hypercube, Data Points Falling, Kind of Generalization, Erecting Bins, D-Dimensional Gaussian Density, Gaussian Kernel
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Recap • We are discussing non-parametric estimation of density functions. PR NPTEL course – p.1/130 Recap • • We are discussing non-parametric estimation of density functions. Here we do not assume any form for the density function. PR NPTEL course – p.2/130 Recap • • • We are discussing non-parametric estimation of density functions. Here we do not assume any form for the density function. The basic idea is to estimate the density by ˆ f (x) = k nV where V is the volume of a small region around x in which k out of the n data samples are found. PR NPTEL course – p.3/130 • The choice of size of V is critical for getting good estimates. PR NPTEL course – p.4/130 • • The choice of size of V is critical for getting good estimates. As discussed in last class there are two possibilities: PR NPTEL course – p.5/130 • • The choice of size of V is critical for getting good estimates. As discussed in last class there are two possibilities: ..

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