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Accord.NET Framework

Scientific computing, machine learning and computer vision for .NET

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Description

The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. This project extends the popular AForge.NET Framework providing a more complete scientific computing environment.

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Features

  • Support Vector Machines (multi-class, mult-label, directed acyclic graphs, ... )
  • Conditional Random Fields and Hidden Conditional Random Fields
  • Continuous and Discrete Hidden Markov Models
  • Standard and Multinomial Logistic Regression
  • Second order Neural Network learning algorithms
  • Statistical Analysis (PCA, LDA, KPCA, KDA, PLS, NMF, ... )
  • Hypothesis Testing (Z, F, T, Wald, Bhapkar, Kappa, Kolmogorov, ... )
  • Decision Trees (including automatic code generation)
  • Discrete and Continuous Naive Bayes Classifiers
  • Gaussian Mixture Models
  • Haar-feature image recognition
  • Camshift object tracking
  • Deep learning

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Additional Project Details

Intended Audience

Science/Research, Developers, Engineering

User Interface

.NET/Mono

Programming Language

C#

Registered

2012-04-08

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