This repository holds my completed Octave/Matlab code for the exercises in the Stanford Machine Learning course, offered on the Coursera platform.
It is based on support vector machines ,first we used linear kernel for lesser featured dataset, Gaussian kernel for more complex dataset. • Machine Learning • In this project contains code and data ...
Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...
The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...
Abstract: Aiming at the problem that the traditional photovoltaic output parametric model presets the distribution and is difficult to describe the meteorological randomness, this paper proposes a ...
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