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Accord.MachineLearning.VectorMachines.Learning Namespace |
Contains algorithms for training Support Vector Machines (SVMs).
Classes| Class | Description | |
|---|---|---|
  | AveragedStochasticGradientDescent |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| AveragedStochasticGradientDescentTKernel |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| AveragedStochasticGradientDescentTKernel, TInput |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| AveragedStochasticGradientDescentTKernel, TInput, TLoss |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss |
Base class for Averaged Stochastic Gradient Descent algorithm implementations.
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| BaseFanChenLinSupportVectorRegressionTModel, TKernel, TInput |
Base class for Fan-Chen-Lin (LibSVM) regression algorithms.
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| BaseLinearCoordinateDescentTModel, TKernel |
Base class for linear coordinate descent learning algorithm.
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| BaseLinearDualCoordinateDescentTModel, TKernel, TInput |
Base class for Linear Dual Coordinate Descent.
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| BaseLinearNewtonMethodTModel, TKernel |
Base class for L2-regularized L2-loss linear support vector classification (primal).
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| BaseLinearNewtonMethodTModel, TKernel, TInput |
L2-regularized L2-loss linear support vector classification (primal).
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| BaseLinearRegressionCoordinateDescentTModel, 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).
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| BaseLinearRegressionNewtonMethodTModel, TKernel, TInput |
Base class for newton method for linear regression learning algorithm.
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| BaseMulticlassSupportVectorLearningTBinary, TKernel, TModel |
Base class for multi-class support vector learning algorithms.
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| BaseMulticlassSupportVectorLearningTInput, TBinary, TKernel, TModel |
Base class for multi-class support vector learning algorithms.
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| BaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel |
Base class for multi-label support vector learning algorithms.
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| BaseOneclassSupportVectorLearningTModel, TKernel, TInput |
One-class Support Vector Machine Learning Algorithm.
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| BaseProbabilisticCoordinateDescentTModel, TKernel, TInput |
Base class for L1-regularized logistic regression (probabilistic SVM) learning algorithm (-s 6).
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| BaseProbabilisticDualCoordinateDescentTModel, TKernel, TInput |
Base class for L2-regularized logistic regression (probabilistic support
vector machine) learning algorithm in the dual form (-s 7).
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| BaseProbabilisticNewtonMethodTModel, TKernel, TInput |
Base class for probabilistic Newton Method learning.
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| BaseSequentialMinimalOptimizationTModel, TKernel, TInput |
Base class for Sequential Minimal Optimization.
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| BaseSequentialMinimalOptimizationRegressionTModel, TKernel, TInput |
Base class for Sequential Minimal Optimization for regression.
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| BaseStochasticGradientDescentTModel, TKernel, TInput, TLoss |
Base class for Averaged Stochastic Gradient Descent algorithm implementations.
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| BaseSupportVectorCalibrationTModel, TKernel, TInput |
Base class for SupportVectorMachine calibration algorithms.
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| BaseSupportVectorClassificationTModel, TKernel, TInput |
Base class for SupportVectorMachine learning algorithms.
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| BaseSupportVectorRegressionTModel, TKernel, TInput |
Base class for SupportVectorMachine regression learning algorithms.
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| FanChenLinSupportVectorRegression |
Support vector regression using FanChenLinQuadraticOptimization (LibSVM) algorithm.
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| FanChenLinSupportVectorRegressionTKernel |
Support vector regression using FanChenLinQuadraticOptimization (LibSVM) algorithm.
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| FanChenLinSupportVectorRegressionTKernel, TInput |
Support vector regression using FanChenLinQuadraticOptimization (LibSVM) algorithm.
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| LeastSquaresLearning |
Least Squares SVM (LS-SVM) learning algorithm.
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| LeastSquaresLearningTKernel, TInput |
Least Squares SVM (LS-SVM) learning algorithm.
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| LeastSquaresLearningBaseTModel, TKernel, TInput |
Base class for Least Squares SVM (LS-SVM) learning algorithm.
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| LinearCoordinateDescent |
L1-regularized L2-loss support vector
Support Vector Machine learning (-s 5).
|
| LinearCoordinateDescentTKernel |
L1-regularized L2-loss support vector
Support Vector Machine learning (-s 5).
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| LinearDualCoordinateDescent |
L2-regularized, L1 or L2-loss dual formulation
Support Vector Machine learning (-s 1 and -s 3).
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| LinearDualCoordinateDescentTKernel |
L2-regularized, L1 or L2-loss dual formulation
Support Vector Machine learning (-s 1 and -s 3).
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| LinearDualCoordinateDescentTKernel, TInput |
L2-regularized, L1 or L2-loss dual formulation
Support Vector Machine learning (-s 1 and -s 3).
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| LinearNewtonMethod |
L2-regularized L2-loss linear support vector classification (primal).
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| LinearNewtonMethodTKernel, TInput |
L2-regularized L2-loss linear support vector classification (primal).
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| LinearRegressionCoordinateDescent |
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).
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| LinearRegressionCoordinateDescentTKernel, 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).
|
| LinearRegressionNewtonMethod |
L2-regularized L2-loss linear support vector regression
(SVR) learning algorithm in the primal formulation (-s 11).
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| LinearRegressionNewtonMethodTKernel, TInput |
L2-regularized L2-loss linear support vector regression
(SVR) learning algorithm in the primal formulation (-s 11).
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| MulticlassSupportVectorLearning | Obsolete.
One-against-one Multi-class Support Vector Machine Learning Algorithm
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| MulticlassSupportVectorLearningTKernel |
One-against-one Multi-class Support Vector Machine Learning Algorithm
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| MulticlassSupportVectorLearningTKernel, TInput |
One-against-one Multi-class Support Vector Machine Learning Algorithm
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| MultilabelSupportVectorLearning | Obsolete.
Obsolete.
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| MultilabelSupportVectorLearningTKernel |
One-against-all Multi-label Support Vector Machine Learning Algorithm
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| MultilabelSupportVectorLearningTKernel, TInput |
One-against-all Multi-label Support Vector Machine Learning Algorithm
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| OneclassSupportVectorLearning | Obsolete.
One-class Support Vector Machine learning algorithm.
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| OneclassSupportVectorLearningTKernel |
One-class Support Vector Machine learning algorithm.
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| OneclassSupportVectorLearningTKernel, TInput |
One-class Support Vector Machine learning algorithm.
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| ProbabilisticCoordinateDescent |
L1-regularized logistic regression (probabilistic SVM)
learning algorithm (-s 6).
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| ProbabilisticCoordinateDescentTKernel, TInput |
L1-regularized logistic regression (probabilistic SVM)
learning algorithm (-s 6).
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| ProbabilisticDualCoordinateDescent |
L2-regularized logistic regression (probabilistic support
vector machine) learning algorithm in the dual form (-s 7).
|
| ProbabilisticDualCoordinateDescentTKernel, TInput |
L2-regularized logistic regression (probabilistic support
vector machine) learning algorithm in the dual form (-s 7).
|
| ProbabilisticNewtonMethod |
L2-regularized L2-loss logistic regression (probabilistic
support vector machine) learning algorithm in the primal.
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| ProbabilisticNewtonMethodTKernel |
L2-regularized L2-loss logistic regression (probabilistic
support vector machine) learning algorithm in the primal.
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| ProbabilisticNewtonMethodTKernel, TInput |
L2-regularized L2-loss logistic regression (probabilistic
support vector machine) learning algorithm in the primal.
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| ProbabilisticOutputCalibration |
Probabilistic Output Calibration for Linear machines.
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| ProbabilisticOutputCalibrationTKernel |
Probabilistic Output Calibration for Kernel machines.
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| ProbabilisticOutputCalibrationTKernel, TInput |
Probabilistic Output Calibration for structured Kernel machines.
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| ProbabilisticOutputCalibrationBaseTModel, TKernel, TInput |
Probabilistic Output Calibration.
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| SequentialMinimalOptimization |
Sequential Minimal Optimization (SMO) Algorithm
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| SequentialMinimalOptimizationTKernel |
Sequential Minimal Optimization (SMO) Algorithm.
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| SequentialMinimalOptimizationTKernel, TInput |
Sequential Minimal Optimization (SMO) Algorithm (for arbitrary data types).
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| SequentialMinimalOptimizationRegression |
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.
|
| SequentialMinimalOptimizationRegressionTKernel |
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.
|
| SequentialMinimalOptimizationRegressionTKernel, 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.
|
| StochasticGradientDescent |
Stochastic Gradient Descent (SGD) for training linear support vector machines.
|
| StochasticGradientDescentTKernel |
Stochastic Gradient Descent (SGD) for training linear support vector machines.
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| StochasticGradientDescentTKernel, TInput |
Stochastic Gradient Descent (SGD) for training linear support vector machines.
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| StochasticGradientDescentTKernel, TInput, TLoss |
Stochastic Gradient Descent (SGD) for training linear support vector machines.
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| SupportVectorReduction |
Exact support vector reduction through linear dependency elimination.
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| SupportVectorReductionTKernel |
Exact support vector reduction through linear dependency elimination.
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| SupportVectorReductionTKernel, TInput |
Exact support vector reduction through linear dependency elimination.
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| SupportVectorReductionBaseTModel, TKernel, TInput |
Exact support vector reduction through linear dependency elimination.
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Interfaces| Interface | Description | |
|---|---|---|
 | ILinearSupportVectorMachineLearning |
Common interface for Support Machine Vector learning algorithms.
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| ISupportVectorMachineLearning |
Common interface for Support Machine Vector learning algorithms.
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| ISupportVectorMachineLearningTInput |
Common interface for Support Machine Vector learning algorithms.
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| ISupportVectorMachineLearningTKernel, TInput |
Common interface for Support Machine Vector learning algorithms.
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Delegates| Delegate | Description | |
|---|---|---|
 | SupportVectorMachineLearningConfigurationFunction | Obsolete.
Obsolete.
|
Enumerations| Enumeration | Description | |
|---|---|---|
 | Loss |
Different categories of loss functions that can be used to learn
support vector machines.
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| SelectionStrategy |
Gets the selection strategy to be used in SMO.
|