kernel-ridge-regression
There are 33 repositories under kernel-ridge-regression topic.
elcorto/pwtools
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.
dralgroup/mlatom
AI-enhanced computational chemistry
binghuang2018/aqml
Amons-based quantum machine learning for quantum chemistry
lsorber/neo-ls-svm
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
Kennethborup/self_distillation
Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression
Arif-PhyChem/MLQD
MLQD is a Python Package for Machine Learning-based Quantum Dissipative Dynamics
qin-yu/julia-regression-boston-housing
Machine learning (linear regression & kernel-ridge regression) examples on the Boston housing dataset
AmishaSomaiya/Machine-Learning
Machine Learning Code Implementations in Python
nickcafferry/Machine-Learning-in-Molecular-Sciences
2017 Summer School on the Machine Learning in the Molecular Sciences. This project aims to help you understand some basic machine learning models including neural network optimization plan, random forest, parameter learning, incremental learning paradigm, clustering and decision tree, etc. based on kernel regression and dimensionality reduction, feature selection and clustering technology.
Arif-PhyChem/Quantum_dissipative_dynamics_with_kernel_methods
Speeding up quantum dissipative dynamics of open systems with kernel methods
sudeshnapal12/Machine-Learning-algorithms-Matlab
Contains ML Algorithms implemented as part of CSE 512 - Machine Learning class taken by Fransico Orabona. Implemented Linear Regression using polynomial basis functions, Perceptron, Ridge Regression, SVM Primal, Kernel Ridge Regression, Kernel SVM, Kmeans.
superlj666/Distributed-Learning-with-Random-Features
Codes and experiments for paper "Distributed Learning with Random Features". Preprint.
windhaunting/kernel_methods
kernel linear regression and svm for Creditcard and Tumor data
butler-julie/SRE
Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning
elcorto/gp_playground
Explore selected topics related to Gaussian processes
jakublala/alchemical-kernels
Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668
lukebella/SpotifyRegression
Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.
tjarkpr/learning-soft-computing-fhwedel
Lecture "Learning & soft computing" @FH-Wedel SS22
EricPaul075/OCP4-Machine-learning-to-predict-energy-consumption
Machine learning regression model to predict energy consumption and GHG emission
jyuan1986/onr2017
Scripts for machine learning at ONR project (2017-)
lanmar/Python---Wine-quality
Cross-validation, knn classif, knn régression, svm à noyau, Ridge à noyau
luansousac/monografia
This repository contains the source code of my bachelors' thesis.
MagneticResonanceImaging/PERK.jl
PERK: Parameter Estimation via Regression with Kernels
mrcreasey/oc-ds-p4-supervised-learning
Anticipate the energy consumption of new commercial buildings
nemolino/TrackPopularityPredictor
SM4ML project
SaharNasiri/housing-price-prediction
House Prices - Advanced Regression Techniques
tjarkpr/software-project-fhwedel
Lecture "Softwareprojekt" @FH-Wedel WS20
anupriya1519/Machine-Learning
Assignments
BenjaminRueling/Red-Wine-Quality
Kernel-Methods on a Red-Wine Dataset
danielchristopher513/Stock_Prediction_Using_Machine_Learning
This repository contains code for predicting stock prices using various machine learning models. The models implemented include Linear Regression, SVM Regression, KNN Regression, Kernel Ridge Regression, and Ridge Regression.
marcosdelcueto/Tutorial_KRR
Codes and images used for blog article at https://www.mdelcueto.com/blog/kernel-ridge-regression-tutorial/