All Posts about Machine Learning

Common tools for function fitting

This post describes the usage of some general tools for the generation and visualization of datasets that represent or approximate mathematical objects such as functions, curves and surfaces; such tools are used by other programs described in other posts on this website.

Fitting functions with a configurable XGBoost regressor

The XGBoost algorithm, known for winning numerous Kaggle competitions, gives incredible results in the fitting of functions; the results are extremely exciting both in terms of error metrics and performance.

Fitting functions with a configurable Support Vector Regressor

The Support Vector Machine algorithms, known to be used in the context of classification, can be used in the regression and in particular in the approximation of both scalar and vector real functions to one or more real variables.

Polynomial regression with Accord.NET

This post shows a use of the PolynomialRegression class of the Accord.NET framework with the aim to demonstrate that classic machine learning polynomial regression can reach interesting levels of accuracy with extremely short learning times.

SMO regression for SVM with PUK kernel in Weka

This post shows how to use Weka, and namely the SMO algorithm for SVM regression with PUK kernel, in order to perform a regression of datasets generated synthetically with one-variable continuous and limited real functions.

SMO forecast for SVM with polynomial kernel in Weka

This post shows how to use Weka, and namely the SMO algorithm for SVM forecast with polynomial kernel, in order to perform a forecast of an univariate synthetic timeseries with periodicity and trend.