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\ Components & Libraries \ Accord.NET Framework
Accord.NET Framework3.3.0 by Cesar Roberto de Souza |
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Software Description:
... several methods for statistical analysis, such as Principal Component Analysis, Linear Discriminant Analysis, Partial Least Squares, Kernel Principal Component Analysis, Kernel Discriminant Analysis, Logistic and Linear Regressions ... >/ Accord.Math Contains a matrix extension library, along with a suite of numerical matrix decomposition ... Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods ... waveforms and audio-related information. Accord.Controls.Vision Windows Forms components and controls to track head, face and hand ...
type: Open Source
Download Accord.NET Frameworkcategories: .NET Framework, C# Framework, AForge.NET Framework, Framework, AForge.NET, .NET Add to Download Basket Report virus or spyware |
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Best Vista Download periodically updates pricing and software information of Accord.NET Framework full version from the publisher,
but some information may be out-of-date. You should confirm all information. Software piracy is theft, using crack, warez passwords, patches, serial numbers, registration codes, key generator, keymaker or keygen for Accord.NET Framework license key is illegal and prevent future development of Accord.NET Framework. Download links are directly from our mirrors or publisher's website, Accord.NET Framework torrent files or shared files from rapidshare, yousendit or megaupload are not allowed! |
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| Released: | September 17, 2016 |
| Filesize: | 483.00 MB |
| Platform: | Windows XP, Windows Vista, Windows Vista x64, Windows 7, Windows 7 x64, Windows 8, Windows 8 x64, Windows 10, Windows 10 x64 |
| Install | Instal And Uninstall |
| Add Your Review or Windows Vista Compatibility Report |
Accord.NET Framework- Releases History |
| Software: | Accord.NET Framework 3.3.0 |
| Date Released: | Sep 17, 2016 |
| Status: | New Release |
| Release Notes: | 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. Imaging: Creating a new Accord.Imaging.Noncommercial assembly to hold non-commercial imaging methods; Adding Fast Guided Filter to Accord.Imaging.Noncommercial. MachineLearning: 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; Statistics: 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. |
| Software: | Accord.NET Framework 2.12.0 |
| Date Released: | Aug 8, 2014 |
| Status: | New Release |
| Software: | Accord.NET Framework 2.9.0 |
| Date Released: | Jul 16, 2013 |
| Status: | New Release |
| Release Notes: | General: · Working on more source code examples for the documentation. Accord.Math: · Adding Levenshtein distance for strings. Accord.Imaging: · Updating BagOfVisualWords to be fully serializable. Accord.MachineLearning: · 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. Accord.Statistics: · 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; |
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