Description:

This 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.

• Accord.Math - Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for contrained and uncontrained problems, special functions and other tools for scientific applications;
• Accord.Statistics - Contains probability distributions, statistical models and methods such as Linear and Logistic regressions, Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods and functions and many other related techniques;
• Accord.Imaging - Interest point detectors (SURF and FAST), image matching and image stitching methods;
• Accord.Neuro - Neural learning algorithms such as Levenberg-Marquardt, Parallel Resilient Backpropagation, initialization procedures such as Nguyen-Widrow and other neural network related methods;
• 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.Vision - Real-time face detection and tracking, as well as general methods for detecting, tracking and transforming objects in image streams. Contains Haar cascade definitions, Camshift and Dynamic Template Matching trackers;
• Accord.Audio - Process, transforms, filters and handle audio signals for machine learning and statistical applications.

For a complete listing of framework features, please see the feature list at http://accord-net.origo.ethz.ch/wiki/features

Packages are also now available through NuGet - http://nuget.org/packages?q=Accord.NET

Changes to previous version:

This release adds support for RProp learning in HCRFs, optimizations to SVM learning and evaluation, a constrained QP solver based on the dual method of Goldfrab and Idnani, robust estimation of fundamental matrices and several other bugfixes and enhancements.

For a complete list of changes, please see the full release notes at http://accord-net.origo.ethz.ch/download/3982

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows

Data Formats: Agnostic

Tags: Svm, Kernel Methods, Algorithms, Classifiers, Statistics, Clustering Algorithm, Probability Estimation, Discriminant Analysis, Wavelet Transform, Principal Component Analysis, Algebra, Fourier Transfo

Archive: download here

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