Machine Learning Toolbox for .NET (C# and Visual Basic)
A number of machine learning algorithms are provided. Every algorithm is optimized for both: memory consumption and execution speed. The range of algorithms spreads from supervised to unsupervised algorithms and each provides a convenient variable parameter list.
| Method name | Short description |
|---|---|
| em | Expectation Maximization - estimate centers and covariance of n multivariate normal distributions according to the samples |
| kmeansclust | k Means Clustering - splits the data into a given number of clusters |
| k Nearest Neighbors - searches k nearest neighbors for every input sample, handles several distances | |
| krr | Kernel Ridge Regression - kernelized version of ridge regression, creates and applies the model with a number of different kernels |
| pca | Principal Component Analysis - finds orthogonal directions used to reduce the dimensionality of the data |
| ridge_regression | Ridge Regression - fits a polynomial model to input data |