Can Accord.NET Be Used in Commercial Applications?

Updated

Yes. Accord.NET is described as a .NET machine learning framework that is "ready to be used in commercial applications," and the site states it is "a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use." The framework is written entirely in C#, and a dedicated License section is available on the website for the specific terms.

What the framework covers

Accord.NET combines machine learning with audio and image processing libraries. According to the site, it includes:

  • Classification — Support Vector Machines, Logistic Regression, Decision Trees, Neural Networks, Deep Learning (Deep Neural Networks), Levenberg-Marquardt with Bayesian Regularization, Restricted Boltzmann Machines, sequence classification, Hidden Markov Classifiers, and Hidden Conditional Random Fields.
  • Regression — Multiple and multivariate linear regression, polynomial regression, logarithmic regression, logistic and multinomial logistic regression (softmax), generalized linear models, plus L1- and L2-regularized logistic regression variants and regression support vector machines.
  • Clustering — K-Means, K-Modes, Mean-Shift, Gaussian Mixture Models, Binary Split, Deep Belief Networks, and Restricted Boltzmann Machines. Clustering can be applied to arbitrary data, including images, data tables, videos, and audio.
  • Distributions — Parametric and non-parametric estimation of more than 40 distributions, including univariate (Normal, Cauchy, Hypergeometric, Poisson, Bernoulli, Kolmogorov-Smirnov, Nakagami, Weibull, Von-Mises) and multivariate (multivariate Normal, Multinomial, Independent, Joint, Mixture) forms.
  • Hypothesis Tests — More than 35 statistical hypothesis tests.

Getting the framework

The site lists these download and setup paths:

  • Download Installer
  • Download Archive
  • Read the Manual
  • Consult the .NET API
  • Fork on GitHub

The current version referenced on the page is GitHub v3.8. Sample applications are provided to help you get up and running quickly, and the documentation and wiki cover the details.

Before you ship

Commercial use is explicitly supported by the framework's own description, but the exact terms (redistribution, attribution, warranty) live in the License section. Check that page for the binding conditions, and confirm the version you download matches the license you reviewed.