This list encompasses the feature set for the current version of the Accord.NET Framework. You may also want to check the feature list for AForge.NET.

• Accord.Math
  • MATLAB-like matrix manipulation of multidimensional-arrays
  • Matrix parsing and formatting (compatible with Octave, MATLAB and C# formats)
  • Limited-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS) method for non-linear optimization
  • Conjugate-Gradient (CG) method for non-linear optimization
  • Numeric matrix decompositions for float (32-bit), double (64-bit) and decimal (128-bit) data-types
    • Nonnegative Matrix Factorization (NMF)
    • Singular Value Decomposition (SVD)
    • Eigenvalue Decomposition (EVD)
    • Generalized Eigenvalue Decomposition (GEVD))
    • Cholesky Decomposition (LLt and LDLt)
    • LU Decomposition
    • QR Decomposition
  • Special scientific functions (gamma, gammal, erf, bessel, ...)
  • Fast Hilbert Transform
  • Wavelet Transforms
    • Haar
    • CDF97

• Accord.Statistics
  • Simple Descriptive Analysis
  • Independent Component Analysis (ICA)
  • Linear Discriminant Analysis (LDA)
  • Kernel Discriminant Analysis (KDA)
  • Principal Component Analysis (PCA)
  • Kernel Principal Component Analysis (KPCA)
  • Partial Least Squares Analysis (PLS)
  • Logistic Regression Fitting and Analysis
  • Stepwise Logistic Regression Analysis
  • Multivariate, Multiple and Simple Linear Regressions
  • Receiver Operating Curves (ROC)
  • Hypothesis Testing (Chi-Square, Wald, Z)
  • Discrete Hidden Markov Models (HMM) and Sequence Classifiers
  • Continuous density Hidden Markov Models and Continuous-density Sequence Classifiers
  • Threshold models for rejection in hidden Markov model sequence classifiers
  • Linear-chain Conditional Random Fields (CRFs)
  • Linear-chain Hidden Conditional Random Fields (HCRFs)
  • Wide variety of Kernels for machine learning applications (including sparse)
  • Probability distributions (univariate and multivariate)
    • Univariate Normal (Gaussian), Bernoulli, Chi-Square, Categorical, Poisson, Von-Mises, Univariate Mixture, InverseGaussian, Nakagami, Rayleigh and Weibull distributions
    • Multivariate Normal (Gaussian), Multinomial, Multivariate Mixture, Joint and Independent distributions
    • Moving/running Circular and Normal statistics
  • Filters for tabular data processing
    • Projection
    • Selection
    • Codification
    • Discretization
    • Normalization
    • Equalization
    • Linear Scaling

• Accord.Neuro
  • Levenberg-Marquardt (with Bayesian Regularization) Learning Algorithm
  • Resilient Backpropagation (RProp)
  • Nguyen-Widrow weight initialization

• Accord.MachineLearning
  • K-Means Clustering
  • Gaussian Mixture Models (GMM)
  • Support Vector Machines (SVM)
  • Kernel Support Vector Machines (kSVM)
  • Multi-class Kernel Support Vector Machines
  • Sequential Minimal Optimization Learning (SMO)
  • Sequential Minimal Optimization for Regression (SMOR)
  • Multipurpose RANSAC for robust model estimation
  • Multipurpose k-fold cross-validation algorithm
  • Generic Grid-Search algorithm for parameter tuning
  • Naïve Bayes for discrete and arbitrary distributions
  • Decision Trees (ID3, C4.5) with on-the-fly code generation

• Accord.Imaging
  • Harris Corner Detector for AForge.NET
  • Features from Accelerated Segment Test (FAST) corners detector
  • Speeded-Up Robust Features (SURF) detector and descriptor
  • Robust Homography estimation using RANSAC
  • Raw and Central Image Moments Calculation
  • Border Following contour extraction
  • K-Curvature algorithm for contour peak detection
  • Blob convex hull defects extraction
  • Filters for image processing
    • Image blending
    • Concatenation
    • Markers (points, pairs, rectangles, features)
    • Wavelet Transform

• Accord.Vision
  • Viola-Jones (Haar) Object Detector (multi-threaded)
  • Object Trackers
    • Camshift Object Tracker
    • HSL Color Range Tracker
    • Template Matching Tracker
  • Controller Components for head and face gestures

• Accord.Audio (experimental)
  • Experimental audio library for audio processing
  • Signal windows (Hann, Blackman, Hamming)
  • Fourier and Hilbert transforms
  • Filters for signal processing
  • Audio depth conversion

• Accord.Audition (experimental)
  • Experimental audio library for computer audition
  • Energy-based simple beat detection

• Samples
  • Sample applications for almost all features listed here.

Libraries marked as (experimental) mean they have not been tested extensively nor their architecture has been proven efficient for their given tasks.