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Accord.NET (logo) Accord.MachineLearning.VectorMachines.Learning Namespace
Contains algorithms for training Support Vector Machines (SVMs).
Classes
  ClassDescription
Public classBaseLinearCoordinateDescentTModel, TKernel
Base class for linear coordinate descent learning algorithm.
Public classBaseLinearDualCoordinateDescentTModel, TKernel, TInput
Base class for Linear Dual Coordinate Descent.
Public classBaseLinearNewtonMethodTModel, TKernel
Base class for L2-regularized L2-loss linear support vector classification (primal).
Public classBaseLinearNewtonMethodTModel, TKernel, TInput
L2-regularized L2-loss linear support vector classification (primal).
Public classBaseLinearRegressionCoordinateDescentTModel, TKernel, TInput
Base class for Coordinate descent algorithm for the L1 or L2-loss linear Support Vector Regression (epsilon-SVR) learning problem in the dual form (-s 12 and -s 13).
Public classBaseLinearRegressionNewtonMethodTModel, TKernel, TInput
Base class for newton method for linear regression learning algorithm.
Public classBaseMulticlassSupportVectorLearningTBinary, TKernel, TModel
Base class for multi-class support vector learning algorithms.
Public classBaseMulticlassSupportVectorLearningTInput, TBinary, TKernel, TModel
Base class for multi-class support vector learning algorithms.
Public classBaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel
Base class for multi-label support vector learning algorithms.
Public classBaseProbabilisticCoordinateDescentTModel, TKernel, TInput
Base class for L1-regularized logistic regression (probabilistic SVM) learning algorithm (-s 6).
Public classBaseProbabilisticDualCoordinateDescentTModel, TKernel, TInput
Base class for L2-regularized logistic regression (probabilistic support vector machine) learning algorithm in the dual form (-s 7).
Public classBaseProbabilisticNewtonMethodTModel, TKernel, TInput
Base class for probabilistic Newton Method learning.
Public classBaseSequentialMinimalOptimizationTModel, TKernel, TInput
Base class for Sequential Minimal Optimization.
Public classBaseSequentialMinimalOptimizationRegressionTModel, TKernel, TInput
Base class for Sequential Minimal Optimization for regression.
Public classBaseSupportVectorCalibrationTModel, TKernel, TInput
Base class for SupportVectorMachine calibration algorithms.
Public classBaseSupportVectorClassificationTModel, TKernel, TInput
Base class for SupportVectorMachine learning algorithms.
Public classBaseSupportVectorRegressionTModel, TKernel, TInput
Base class for SupportVectorMachine regression learning algorithms.
Public classLeastSquaresLearning
Least Squares SVM (LS-SVM) learning algorithm.
Public classLeastSquaresLearningTKernel, TInput
Least Squares SVM (LS-SVM) learning algorithm.
Public classLeastSquaresLearningBaseTModel, TKernel, TInput
Base class for Least Squares SVM (LS-SVM) learning algorithm.
Public classLinearCoordinateDescent
L1-regularized L2-loss support vector Support Vector Machine learning (-s 5).
Public classLinearCoordinateDescentTKernel
L1-regularized L2-loss support vector Support Vector Machine learning (-s 5).
Public classCode exampleLinearDualCoordinateDescent
L2-regularized, L1 or L2-loss dual formulation Support Vector Machine learning (-s 1 and -s 3).
Public classCode exampleLinearDualCoordinateDescentTKernel
L2-regularized, L1 or L2-loss dual formulation Support Vector Machine learning (-s 1 and -s 3).
Public classCode exampleLinearDualCoordinateDescentTKernel, TInput
L2-regularized, L1 or L2-loss dual formulation Support Vector Machine learning (-s 1 and -s 3).
Public classLinearNewtonMethod
L2-regularized L2-loss linear support vector classification (primal).
Public classLinearNewtonMethodTKernel, TInput
L2-regularized L2-loss linear support vector classification (primal).
Public classLinearRegressionCoordinateDescent
Coordinate descent algorithm for the L1 or L2-loss linear Support Vector Regression (epsilon-SVR) learning problem in the dual form (-s 12 and -s 13).
Public classLinearRegressionCoordinateDescentTKernel, TInput
Coordinate descent algorithm for the L1 or L2-loss linear Support Vector Regression (epsilon-SVR) learning problem in the dual form (-s 12 and -s 13).
Public classLinearRegressionNewtonMethod
L2-regularized L2-loss linear support vector regression (SVR) learning algorithm in the primal formulation (-s 11).
Public classLinearRegressionNewtonMethodTKernel, TInput
L2-regularized L2-loss linear support vector regression (SVR) learning algorithm in the primal formulation (-s 11).
Public classCode exampleMulticlassSupportVectorLearning Obsolete.
One-against-one Multi-class Support Vector Machine Learning Algorithm
Public classCode exampleMulticlassSupportVectorLearningTKernel
One-against-one Multi-class Support Vector Machine Learning Algorithm
Public classCode exampleMulticlassSupportVectorLearningTKernel, TInput
One-against-one Multi-class Support Vector Machine Learning Algorithm
Public classMultilabelSupportVectorLearning Obsolete.
Obsolete.
Public classCode exampleMultilabelSupportVectorLearningTKernel
One-against-all Multi-label Support Vector Machine Learning Algorithm
Public classCode exampleMultilabelSupportVectorLearningTKernel, TInput
One-against-all Multi-label Support Vector Machine Learning Algorithm
Public classOneclassSupportVectorLearning Obsolete.
One-class Support Vector Machine Learning Algorithm.
Public classOneclassSupportVectorLearningTKernel, TInput
One-class Support Vector Machine Learning Algorithm.
Public classOneclassSupportVectorLearningTModel, TKernel, TInput
One-class Support Vector Machine Learning Algorithm.
Public classProbabilisticCoordinateDescent
L1-regularized logistic regression (probabilistic SVM) learning algorithm (-s 6).
Public classProbabilisticCoordinateDescentTKernel, TInput
L1-regularized logistic regression (probabilistic SVM) learning algorithm (-s 6).
Public classProbabilisticDualCoordinateDescent
L2-regularized logistic regression (probabilistic support vector machine) learning algorithm in the dual form (-s 7).
Public classProbabilisticDualCoordinateDescentTKernel, TInput
L2-regularized logistic regression (probabilistic support vector machine) learning algorithm in the dual form (-s 7).
Public classProbabilisticNewtonMethod
L2-regularized L2-loss logistic regression (probabilistic support vector machine) learning algorithm in the primal.
Public classProbabilisticNewtonMethodTKernel
L2-regularized L2-loss logistic regression (probabilistic support vector machine) learning algorithm in the primal.
Public classProbabilisticNewtonMethodTKernel, TInput
L2-regularized L2-loss logistic regression (probabilistic support vector machine) learning algorithm in the primal.
Public classCode exampleProbabilisticOutputCalibration
Probabilistic Output Calibration for Linear machines.
Public classCode exampleProbabilisticOutputCalibrationTKernel
Probabilistic Output Calibration for Kernel machines.
Public classCode exampleProbabilisticOutputCalibrationTKernel, TInput
Probabilistic Output Calibration for structured Kernel machines.
Public classProbabilisticOutputCalibrationBaseTModel, TKernel, TInput
Probabilistic Output Calibration.
Public classCode exampleSequentialMinimalOptimization
Sequential Minimal Optimization (SMO) Algorithm
Public classCode exampleSequentialMinimalOptimizationTKernel
Sequential Minimal Optimization (SMO) Algorithm.
Public classCode exampleSequentialMinimalOptimizationTKernel, TInput
Sequential Minimal Optimization (SMO) Algorithm (for arbitrary data types).
Public classCode exampleSequentialMinimalOptimizationRegression
Sequential Minimal Optimization (SMO) Algorithm for Regression. Warning: this code is contained in a GPL assembly. Thus, if you link against this assembly, you should comply with the GPL license.
Public classCode exampleSequentialMinimalOptimizationRegressionTKernel
Sequential Minimal Optimization (SMO) Algorithm for Regression. Warning: this code is contained in a GPL assembly. Thus, if you link against this assembly, you should comply with the GPL license.
Public classCode exampleSequentialMinimalOptimizationRegressionTKernel, TInput
Sequential Minimal Optimization (SMO) Algorithm for Regression. Warning: this code is contained in a GPL assembly. Thus, if you link against this assembly, you should comply with the GPL license.
Public classCode exampleSupportVectorReduction
Exact support vector reduction through linear dependency elimination.
Public classCode exampleSupportVectorReductionTKernel
Exact support vector reduction through linear dependency elimination.
Public classCode exampleSupportVectorReductionTKernel, TInput
Exact support vector reduction through linear dependency elimination.
Public classCode exampleSupportVectorReductionBaseTModel, TKernel, TInput
Exact support vector reduction through linear dependency elimination.
Interfaces
Delegates
  DelegateDescription
Public delegateSupportVectorMachineLearningConfigurationFunction Obsolete.
Obsolete.
Enumerations
  EnumerationDescription
Public enumerationLoss
Different categories of loss functions that can be used to learn support vector machines.
Public enumerationSelectionStrategy
Gets the selection strategy to be used in SMO.