There are 1 repository under kernel-regression topic.
pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.
Machine-Learning-Regression
Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms
Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)
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Train a neural network in feature and lazy regimes on a regression task defined on the hyper-sphere.
Implementation of various Machine Learning Algorithms and Machine Learning Concepts in Python
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.
Guide for the Baccarelli Lab GitHub
My realization of kernel regression.
A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.
Anisotropic smoothing for change-point regression data
Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling
Implementation of a Gaussian Kernel Regression for Temperature prediction using PySpark.
This R package repository performs optimal transport and kernel regression hypothesis testing. Functions to perform large scale simulations are also provided.
UCL COMP0081 Applied Machine Learning (2023/24)
Nonparametric regression examples with R and Python
This repo contains an R package to execute ROKET's real data analysis workflow on TCGA cancer types
I performed the task of multilinear and generalized regression using both polynomial and non polynomial kernels