kernel-ridge-regression | Topic | Ecosyste.ms: Repos

Topic: "kernel-ridge-regression"

dralgroup/mlatom

AI-enhanced computational chemistry

Language: Python - Size: 194 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 70 - Forks: 11

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.

Language: Python - Size: 21.9 MB - Last synced at: 25 days ago - Pushed at: 9 months ago - Stars: 66 - Forks: 15

lsorber/neo-ls-svm

Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

Language: Python - Size: 321 KB - Last synced at: 4 days ago - Pushed at: about 1 year ago - Stars: 33 - Forks: 3

binghuang2018/aqml

Amons-based quantum machine learning for quantum chemistry

Language: Python - Size: 34.2 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 18 - Forks: 4

Kennethborup/self_distillation

Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression

Language: Jupyter Notebook - Size: 1.43 MB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 15 - Forks: 0

Arif-PhyChem/MLQD

MLQD is a Python Package for Machine Learning-based Quantum Dissipative Dynamics

Language: Jupyter Notebook - Size: 34.2 MB - Last synced at: 9 days ago - Pushed at: 8 months ago - Stars: 14 - Forks: 4

qin-yu/julia-regression-boston-housing

Machine learning (linear regression & kernel-ridge regression) examples on the Boston housing dataset

Language: Julia - Size: 591 KB - Last synced at: 26 days ago - Pushed at: over 6 years ago - Stars: 11 - Forks: 4

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.

Language: Jupyter Notebook - Size: 109 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 5 - Forks: 1

AmishaSomaiya/Machine-Learning

Machine Learning Code Implementations in Python

Language: Python - Size: 36.9 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 4 - Forks: 0

superlj666/Distributed-Learning-with-Random-Features

Codes and experiments for paper "Distributed Learning with Random Features". Preprint.

Language: MATLAB - Size: 327 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 1

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.

Language: TeX - Size: 27.9 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 3 - Forks: 3

windhaunting/kernel_methods

kernel linear regression and svm for Creditcard and Tumor data

Language: Python - Size: 30.3 KB - Last synced at: about 2 years ago - Pushed at: over 7 years ago - Stars: 2 - Forks: 2

elcorto/gp_playground

Explore selected topics related to Gaussian processes

Language: Python - Size: 57.8 MB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 1 - Forks: 0

jakublala/alchemical-kernels

Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668

Language: Python - Size: 1.13 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

charumakhijani/advanced-house-price-prediction

Language: Jupyter Notebook - Size: 1.66 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 1

JakubMartinka/Fulvene-ML-FSSH

Repository associated with article "Machine Learning of Nonadiabatic Coupling Vectors is Easier Than Energies"

Size: 1.95 KB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 0 - Forks: 0

AntoniaSavu/Kernel-Based-Molecular-ML-For-Vector-Valued-Properties

Language: Jupyter Notebook - Size: 9.97 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

MagneticResonanceImaging/PERK.jl

PERK: Parameter Estimation via Regression with Kernels

Language: Julia - Size: 331 KB - Last synced at: 7 days ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

kuulia/CODE

Part of Bachelor's Thesis: Feature engineering for machine learning predictions in atmospheric science. Main code for generating molecular descriptors and training Kernel Ridge Regression ML-model and testing.

Language: Python - Size: 48.5 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

lukebella/SpotifyRegression

Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.

Language: Jupyter Notebook - Size: 2.43 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

nemolino/TrackPopularityPredictor

Statistical Methods for Machine Learning project

Language: Jupyter Notebook - Size: 16.3 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 146 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0