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.
- 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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