This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

This repository contains MATLAB scripts and sample data for applying denoising method presented in: "Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data"

A multiprocess MATLAB toolbox for parallel hyperparamater tuning for Machine Learning applications

This toolbox contains 6 types of neural networks, which is simple and easy to implement.

This is a reverse algorithem for GCV method that removes the signal and keep the background noise

LDA, KNN, GA, K-means on Iris, Sonar and USPS datasets.

Controlled importance-weighted cross-validation

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

This toolbox offers convolution neural networks (CNN) using k-fold cross-validation, which are simple and easy to implement.

Machine learning pipelines with K-fold cross-validation library for FC data

Detects false banknotes in the UC Irvine dataset.

GEARS a toolbox for Global parameter Estimation with Automated Regularisation via Sampling by Jake Alan Pitt and Julio R. Banga

Regularization parameter estimation under covariate shift

Sensor and source-space MVPA analysis scripts for MEG data processed with Fieldtrip.

Recreating Chapter 1 topics from PRML. Curve fitting with matlab. Manual code for cross validation.

Wine Goodness Prediction

An implementation of Artificial Neural Network from scratch (in MATLAB)

Bias/Variance dilemma, cross-validation and work on Iris Data Set from UCI Machine Learning Repository

Scratching the holy-grail of cross validation