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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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| AveragedStochasticGradientDescent<TKernel> |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| AveragedStochasticGradientDescent<TKernel, TInput> |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| AveragedStochasticGradientDescent<TKernel, TInput, TLoss> |
Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.
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| BaseAveragedStochasticGradientDescent<TModel, TKernel, TInput, TLoss> |
Base class for Averaged Stochastic Gradient Descent algorithm implementations.
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| BaseFanChenLinSupportVectorRegression<TModel, TKernel, TInput> |
Base class for Fan-Chen-Lin (LibSVM) regression algorithms.
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| BaseLinearCoordinateDescent<TModel, TKernel> |
Base class for linear coordinate descent learning algorithm.
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| BaseLinearDualCoordinateDescent<TModel, TKernel, TInput> |
Base class for Linear Dual Coordinate Descent.
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| BaseLinearNewtonMethod<TModel, TKernel> |
Base class for L2-regularized L2-loss linear support vector classification (primal).
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| BaseLinearNewtonMethod<TModel, TKernel, TInput> |
L2-regularized L2-loss linear support vector classification (primal).
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| BaseLinearRegressionCoordinateDescent<TModel, 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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| BaseLinearRegressionNewtonMethod<TModel, TKernel, TInput> |
Base class for newton method for linear regression learning algorithm.
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| BaseMulticlassSupportVectorLearning<TBinary, TKernel, TModel> |
Base class for multi-class support vector learning algorithms.
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| BaseMulticlassSupportVectorLearning<TInput, TBinary, TKernel, TModel> |
Base class for multi-class support vector learning algorithms.
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| BaseMultilabelSupportVectorLearning<TInput, TBinary, TKernel, TModel> |
Base class for multi-label support vector learning algorithms.
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| BaseOneclassSupportVectorLearning<TModel, TKernel, TInput> |
One-class Support Vector Machine Learning Algorithm.
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| BaseProbabilisticCoordinateDescent<TModel, TKernel, TInput> |
Base class for L1-regularized logistic regression (probabilistic SVM) learning algorithm (-s 6).
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| BaseProbabilisticDualCoordinateDescent<TModel, 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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| BaseProbabilisticNewtonMethod<TModel, TKernel, TInput> |
Base class for probabilistic Newton Method learning.
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| BaseSequentialMinimalOptimization<TModel, TKernel, TInput> |
Base class for Sequential Minimal Optimization.
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| BaseSequentialMinimalOptimizationRegression<TModel, TKernel, TInput> |
Base class for Sequential Minimal Optimization for regression.
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| BaseStochasticGradientDescent<TModel, TKernel, TInput, TLoss> |
Base class for Averaged Stochastic Gradient Descent algorithm implementations.
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| BaseSupportVectorCalibration<TModel, TKernel, TInput> |
Base class for SupportVectorMachine calibration algorithms.
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| BaseSupportVectorClassification<TModel, TKernel, TInput> |
Base class for SupportVectorMachine learning algorithms.
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| BaseSupportVectorRegression<TModel, 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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| FanChenLinSupportVectorRegression<TKernel> |
Support vector regression using FanChenLinQuadraticOptimization (LibSVM) algorithm.
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| FanChenLinSupportVectorRegression<TKernel, TInput> |
Support vector regression using FanChenLinQuadraticOptimization (LibSVM) algorithm.
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| LeastSquaresLearning |
Least Squares SVM (LS-SVM) learning algorithm.
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| LeastSquaresLearning<TKernel, TInput> |
Least Squares SVM (LS-SVM) learning algorithm.
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| LeastSquaresLearningBase<TModel, 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).
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| LinearCoordinateDescent<TKernel> |
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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| LinearDualCoordinateDescent<TKernel> |
L2-regularized, L1 or L2-loss dual formulation
Support Vector Machine learning (-s 1 and -s 3).
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| LinearDualCoordinateDescent<TKernel, 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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| LinearNewtonMethod<TKernel, 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).
|
| LinearRegressionCoordinateDescent<TKernel, 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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| LinearRegressionNewtonMethod<TKernel, 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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| MulticlassSupportVectorLearning<TKernel> |
One-against-one Multi-class Support Vector Machine Learning Algorithm
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| MulticlassSupportVectorLearning<TKernel, TInput> |
One-against-one Multi-class Support Vector Machine Learning Algorithm
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| MultilabelSupportVectorLearning | Obsolete.
Obsolete.
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| MultilabelSupportVectorLearning<TKernel> |
One-against-all Multi-label Support Vector Machine Learning Algorithm
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| MultilabelSupportVectorLearning<TKernel, 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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| OneclassSupportVectorLearning<TKernel> |
One-class Support Vector Machine learning algorithm.
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| OneclassSupportVectorLearning<TKernel, 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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| ProbabilisticCoordinateDescent<TKernel, 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).
|
| ProbabilisticDualCoordinateDescent<TKernel, 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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| ProbabilisticNewtonMethod<TKernel> |
L2-regularized L2-loss logistic regression (probabilistic
support vector machine) learning algorithm in the primal.
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| ProbabilisticNewtonMethod<TKernel, 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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| ProbabilisticOutputCalibration<TKernel> |
Probabilistic Output Calibration for Kernel machines.
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| ProbabilisticOutputCalibration<TKernel, TInput> |
Probabilistic Output Calibration for structured Kernel machines.
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| ProbabilisticOutputCalibrationBase<TModel, TKernel, TInput> |
Probabilistic Output Calibration.
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| SequentialMinimalOptimization |
Sequential Minimal Optimization (SMO) Algorithm
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| SequentialMinimalOptimization<TKernel> |
Sequential Minimal Optimization (SMO) Algorithm.
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| SequentialMinimalOptimization<TKernel, 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.
|
| SequentialMinimalOptimizationRegression<TKernel> |
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.
|
| SequentialMinimalOptimizationRegression<TKernel, 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.
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| StochasticGradientDescent<TKernel> |
Stochastic Gradient Descent (SGD) for training linear support vector machines.
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| StochasticGradientDescent<TKernel, TInput> |
Stochastic Gradient Descent (SGD) for training linear support vector machines.
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| StochasticGradientDescent<TKernel, 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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| SupportVectorReduction<TKernel> |
Exact support vector reduction through linear dependency elimination.
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| SupportVectorReduction<TKernel, TInput> |
Exact support vector reduction through linear dependency elimination.
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| SupportVectorReductionBase<TModel, 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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| ISupportVectorMachineLearning<TInput> |
Common interface for Support Machine Vector learning algorithms.
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| ISupportVectorMachineLearning<TKernel, TInput> |
Common interface for Support Machine Vector learning algorithms.
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Delegates| Delegate | Description | |
|---|---|---|
 | SupportVectorMachineLearningConfigurationFunction | Obsolete.
Obsolete.
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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.
|