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Showing content from http://accord-framework.net/docs/html/R_Project_Accord_NET.htm below:

Accord.NET provides statistical analysis, machine learning, image processing and computer vision methods for .NET applications. Once an extension to the former AForge.NET Framework, the framework grew to incorporate AForge.NET and complement it with new features, adding to a more complete environment for scientific computing in .NET.

The framework is divided in libraries, available through an executable installer, standalone compressed archives and NuGet packages. Those libraries are divided among three main functionalities, listed below:

• Accord.Math - Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for constrained and unconstrained problems, special functions and other tools for scientific applications.
• Accord.Statistics - Contains probability distributions, hypothesis testing, statistical models and methods such as Linear and Logistic regression, Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods and many other related techniques.
• Accord.MachineLearning - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications.
• Accord.Neuro - Neural learning algorithms such as Levenberg-Marquardt, Parallel Resilient Backpropagation, the Nguyen-Widrow initialization algorithm, Deep Belief Networks and Restrictured Boltzmann Machines, and many other neural network related items.

• Accord.Imaging - Contains interest point detectors (such as Harris, SURF, FAST and FREAK), image filters, image matching and image stitching methods, as well as feature extractors such as Histograms of Oriented Gradients and Haralick's textural feature descriptors.
• Accord.Audio - Contains methods to process, transforms, filters and handle audio signals for machine learning and statistical applications.
• Accord.Vision - Real-time face detection and tracking, as well as general methods for detecting, tracking and transforming objects in image streams. Contains cascade definitions, Camshift and Dynamic Template Matching trackers.

• Accord.Controls - Histograms, scatterplots and tabular data viewers for scientific applications.
• Accord.Controls.Imaging - Windows Forms controls to show and handle images. Contains a convenient ImageBox control which mimics the traditional MessageBox for quickly displaying or inspecting images.
• Accord.Controls.Audio - Windows Forms controls to display waveforms and audio-related information.
• Accord.Controls.Vision - Windows Forms components and controls to track head, face and hand movements and other computer vision related tasks.

A complete listing of the framework's namespaces is presented below. Please click on any of the namespace names for more details.

Contains frequency-domain signal filters.

Contains time-domain signal processing filters.

Contains specialized signal generators. Generate square signals, sinusoids, pulse and other filters for use in signal processing.

Contains audio window functions which can be used to split signals in time.

Contains beat detection algorithms and related methods.

Contains collections such as Lists, Dictionaries, Trees and other useful structures.

Contains audio devices to reproduce and capture sounds exposed through DirectSound.

Contains classes and methods to convert between different image representations, such as between common images, numeric matrices and arrays.

Contains the image processing filters such as the Wavelet transform, stereo rectification, image blending and point markers.

Contains image moments calculators such as central and raw moments,

Contains discrete and continuous density Naive Bayes models for pattern recognition and concept learning. Supports a wide diversity of probabilistic distributions.

Contains Boosting related techniques for creating classifier ensembles and other composition models.

Contains Boosting related techniques for creating classifier ensembles and other composition models.

Contains discrete and continuous Decision Trees, with support for automatic code generation, tree pruning and the creation of

.

Contains learning algorithms for inducing

.

Contains classes to prune decision trees, removing unneeded nodes in an attempt to improve generalization.

Contains methods for robust estimation of geometry entities.

Contains classes related to

(SVMs). Contains

,

,

, SVM-DAGs (Directed Acyclic Graphs),

and also offers support for the

of SVM outputs.

Contains algorithms for training Support Vector Machines (SVMs).

Comparison methods that can be used in sort algorithms such as Sort(Array).

Contains numerical decompositions such as

,

,

,

, and

with specialized definitions for most .NET data types: float, double, and decimals.

Contains methods for the automatic differentiation of mathematical formulas, such as the Finite Differences method.

Contains algorithm environments you can inherit from and let your code be similar to famous environments such as R and Octave.

Contains geometry-related classes. Can identify convex-hulls, detect curvatures and extract convexity defects. When used together with the Imaging and Vision namespaces, can create finger detection components.

Numerical methods for approximating integrals.

Contains classes to model complex kinematic chains, useful for robotic applications.

Contains classes for constrained and unconstrained optimization. Includes

,

and

, gradient-free optimization methods such as

and the

solver for Quadratic Programming (QP) problems.

Contains Wavelet transforms such as the Cohen-Daubechies-Feauveau and the Haar Wavelet transforms.

Contains different activation functions for artificial neurons.

Contains different layer architecures for artificial neural networks.

Contains neural network learning algorithms such as the Levenberg-Marquardt (LM) with Bayesian Regularization and the Resilient Backpropagation (RProp) for multi-layer networks. This namespace extends the AForge.Neuro namespace of the AForge.NET project.

Contains different neural network architectures, such as specialized architectures for deep learning and Boltzmann machines.

Contains different kinds of artificial neurons.

Contains methods to visualize information drawn from neural networks.

Contains many statistical analysis, such as

,

,

,

,

,

,

and

. Also contains performance assessment analysis such as

and

.

Contains contrast functions to be used with Independent Component Analysis (ICA).

Contains more than 40 statistical distributions, with support for most probability distribution measures and estimation methods.

Contains density estimation kernels which can be used in combination with

and

.

Contains special options which can be used in distribution fitting (estimation) methods.

Contains a multivariate distributions such as the

,

,

,

and

.

Contains univariate distributions such as

,

,

,

,

, and specialized distributions such as the

,

,

, and

distributions.

Contains data processing filters, such as data normalization, discretization, equalization, selection and projection filters.

Contains more than 30+ kernel functions for machine learning and statistical applications. Kernel functions are used in kernel methods such as the Support Vector Machine (SVM).

Contains kernel function able to deal with sparse data in LibSVM's format.

Contains link functions for generalized linear models, such as the Logit, the Probit and Cauchit link functions.

Contains statistical models with direct applications in machine learning, such as

,

,

and

and

.

Contains classes related to

,

and their

.

Contains CRF feature functions such as Emission, Transition, First and Second Moments features.

Contains potential functions for CRFs and HCRFs.

Contains learning algorithms for

and

, such as

,

and

learning.

Contains classes related to Hidden Markov Models and their learning algorithms. Offers support for both discrete and continuous-density models, as well as Markov classifiers and threshold models for sequence rejection.

Contains learning algorithms such as Baum-Welch.

Contains topologies for HMMs, such as Forward-only and Ergodic topologies.

Contains statistical regression models such as logistic and linear regressions.

Fitting (learning) algorithms for regression models, such as the Iterative Reweighted Least Squares for standard logistic regressors and the Lower-Bound approximator for multinomial logistic regression.

Linear statistical regression models such as simple, polynomial, multiple and multivariate linear regressions.

Contains classes to estimate moving statistics, i.e. statistics computed within a time frame window.

Contains classes to estimate running statistics, i.e. statistics which should be computed and updated as soon as new data becomes available.

Contains 34+ statistical hypothesis tests, including

and

, non-parametric tests such as the

and the

,

tests such as the

, including variations for

, as well as the

and

tests; and the more traditional

,

,

,

and

.

Contains methods for power analysis of several related hypothesis tests, including support for automatic sample size estimation.

Contains classes for statistical visualization such as Histograms and Scatterplots.

Contains object detectors such as the Viola-Jones (Haar feature) method. The Haar cascades are completely compatible with OpenCV generated definitions and the assembly comes with direct support for bundled definitions for face and nose templates.

Built-in Haar cascade definitions to use with the Haar feature object detector. Those definitions can be called directly from code without need for loading XML files.

Contains classes for object tracking. Include the Camshift algorithm, color segmentation-based trackers and dynamic template matching trackers.

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