A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.
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Updated
Feb 9, 2017
A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.
Nonparametric regression examples with R and Python
This R package repository performs optimal transport and kernel regression hypothesis testing. Functions to perform large scale simulations are also provided.
My realization of kernel regression.
Assess Balance with Machine Learning
This repo contains an R package to execute ROKET's real data analysis workflow on TCGA cancer types
Shared Bike Volumn Prediction
Anisotropic smoothing for change-point regression data
Guide for the Baccarelli Lab GitHub
Implementation of a Gaussian Kernel Regression for Temperature prediction using PySpark.
Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling
My implementation of some algorithms
Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning
This is the recent work of my on the importance and application of mathematical function around its Hilbert function theory on artificial intelligence algorithms. The main motivation was the desire of improving the convergence rate and learning rate of various learning algorithms via Generalized Gaussian Radial Basis Function.
Implementation of various Machine Learning Algorithms and Machine Learning Concepts in Python
Train a neural network in feature and lazy regimes on a regression task defined on the hyper-sphere.
Tool for non-parametric curve fitting using local polynomials.
For quick search
Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)
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