Machine Learning
Experiment on Machine Learning
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.
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.
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.
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.
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.