Accord.NET Framework 3.3.0 freeware
... and computer vision. It includes several methods for statistical analysis, such as Principal Component Analysis, Linear Discriminant Analysis, Partial Least Squares. ...
|Author||Cesar Roberto de Souza|
|OS||Windows XP, Windows Vista, Windows Vista x64, Windows 7, Windows 7 x64, Windows 8, Windows 8 x64, Windows 10, Windows 10 x64|
|Installation||Instal And Uninstall|
|Keywords||.NET Framework, C# Framework, AForge.NET Framework, Framework, AForge.NET, .NET|
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|3.3.0||Sep 17, 2016||New Release||General:
This will be last release that includes an executable installer. If you are still using the installer, please move to NuGet or use the framework compressed archive files.
Creating a new Accord.Imaging.Noncommercial assembly to hold non-commercial imaging methods;
Adding Fast Guided Filter to Accord.Imaging.Noncommercial.
Fixing Binary Split's learn method to accept null weights;
Updating Binary Split example to reflect the new API;
Adding constructors to allow tree inducing algorithms to create a tree from scratch;
Fixing multiple issues with statistical analyses classes when they are used using the new classification/regression APIs;
Statistical measures (Measures.cs) have been moved to the Accord.Math assembly,
but have been kept under the Accord.Statistics namespace;
Correcting L2-regularization in Logistic Regression.
|2.12.0||Aug 8, 2014||New Release|
|2.9.0||Jul 16, 2013||New Release||General:
· Working on more source code examples for the documentation.
· Adding Levenshtein distance for strings.
· Updating BagOfVisualWords to be fully serializable.
· Adding K-dimensional trees (K-d trees);
· Adding Mean-Shift clustering algorithm;
· Adding support for weights in Gaussian Mixture Models;
· Correcting the name of the K-Nearest Neighbors algorithm;
· Improving K-Nearest Neighbors for double using a K-d tree;
· Changing K-Nearest Neighbors generic argument to represent the
· instance type rather than the type of the array of instances.
· Adding interfaces for density estimation kernels;
· Adding Gaussian, Epanechnikov, Uniform density kernels;
· Adding Bartlett's and Levene's tests for variances;
· Adding hypothesis tests for comparing ROC curves;
· Adding support for scatterplot generation directly from ROC curves;
· Adding running Markov models and running Markov classifier filters;