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Accord.NET Framework 2.3.0



 

Accord.NET Framework 2.3.0

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Accord.NET Framework 2.3.0 Ranking & Summary

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Ranking Level
User Review: 8 (2 times)
File size: 35.9 MB
Platform: Windows All
License: LGPL
Price: FREE
Downloads: 205
Date added: 2011-08-31

Accord.NET Framework 2.3.0 description

Accord.NET Framework 2.3.0 brings you an advanced and convenient to use C# framework extending the excellent AForge.NET Framework with new tools and libraries. Accord.NET provides many algorithms for many topics in mathematics, statistics, machine learning, artificial intelligence and computer vision. It includes several methods for statistical analysis, such as Principal Component Analysis, Linear Discriminant Analysis, Partial Least Squares, Kernel Principal Component Analysis, Kernel Discriminant Analysis, Logistic and Linear Regressions and Receiver-Operating Curves. It also includes machine learning topics such as (Kernel) Support Vector Machines, Bayesian regularization for Neural Network training, RANSAC, K-Means, Gaussian Mixture Models and Discrete and Continuous Hidden Markov Models. 

The framework is comprised by libraries and sample applications demonstrating their features. Some of the libraries include:

  • Accord.Math - General math and matrix library with numerical decompositions and related linear algebra methods;
  • Accord.Statistics - library with statistical analysis and related tools;
  • Accord.Imaging - extension to the AForge.NET Imaging library with new filters and routines;
  • Accord.Neuro - extension to the AForge.NET Neuro library with other learning algorithms;
  • Accord.MachineLearning - extension to AForge's machine learning library with SVMs and related techniques;
  • Accord.Vision - extension to the AForge.NET Vision library with realtime object detectors and trackers;
  • Accord.Audio - experimental library with signal filters and audio processing routines.

Major Features:

  1. 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
      • Numeric matrix decompositions
      • 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
  2. 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
    • 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
      • Multivariate Normal (Gaussian), Multinomial, Multivariate Mixture
      • Moving/running Circular and Normal statistics
    • Filters for tabular data processing
      • Projection
      • Selection
      • Codification
      • Discretization
      • Normalization
      • Equalization
      • Linear Scaling
  3. Accord.Neuro
    • Levenberg-Marquardt (with Bayesian Regularization) Learning Algorithm 
    • Nguyen-Widrow Weight Initialization for Activation Networks
  4. 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
  5. 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
  6. Accord.Vision
    • Viola-Jones (Haar) Object Detector
    • Object Trackers
      • Camshift Object Tracker
      • HSL Color Range Tracker
      • Template Matching Tracker
    • Controller Components for head and face gestures 
  7. 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
  8. Accord.Audition (experimental)
    • Experimental audio library for computer audition
    • Energy-based simple beat detection 

Requirements:

  • NET Framework
  • AForge.NET Framework 

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