Kernel k Nearest Neighbors in R

Dimensionality Reduction via Regression using Kernel Ridge Regression in R

R package for KLIC: Kernel Learning Integrative Clustering

Classic Machine Learning in R

Kernel Functions and Tools for Machine Learning Applications

A Collection of Kernel and Distance Methods for Statistical Inference

Code for the Paper "Evaluating Independence and Conditional Independence Measures"

A mathematical analysis and implementation of kernel PCA 🤖

Solves kernel ridge regression within the the mixed model framework. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available.

We compared the predictive accuracy and sparsity of support vector machines and relevance vector machines for a range of synthetic data sets differing in signal-to-noise ratio and other measures of difficulty.

This function fit a Multi-class Kernel Logistic Regression model to the data. The return list contains the estimated kernel weights as well as the original data to perform predictions.There are two types of kernel, they are 'RBF' and 'polynomial'.

Machine learning educational projects.

Functions for conducting regression estimation on nonsmooth data