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Accord.NET Framework

Scientific computing, machine learning and computer vision for .NET

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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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  • 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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Intended Audience

Science/Research, Developers, Engineering

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