AveragedStochasticGradientDescentTKernel, TInput Class

Averaged Stochastic Gradient Descent (ASGD) for training linear support vector machines.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningBinaryLearningBaseSupportVectorMachineTKernel, TInput, TInput
    Accord.MachineLearning.VectorMachines.LearningBaseAveragedStochasticGradientDescentSupportVectorMachineTKernel, TInput, TKernel, TInput, HingeLoss
      Accord.MachineLearning.VectorMachines.LearningAveragedStochasticGradientDescentTKernel, TInput

Namespace: Accord.MachineLearning.VectorMachines.Learning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

Copy

public sealed class AveragedStochasticGradientDescent<TKernel, TInput> : BaseAveragedStochasticGradientDescent<SupportVectorMachine<TKernel, TInput>, TKernel, TInput, HingeLoss>
where TKernel : struct, new(), ILinear<TInput>
where TInput : IList, ICloneable

Public NotInheritable Class AveragedStochasticGradientDescent(Of TKernel As {Structure, New, ILinear(Of TInput)}, TInput As {IList, ICloneable})
	Inherits BaseAveragedStochasticGradientDescent(Of SupportVectorMachine(Of TKernel, TInput), TKernel, TInput, HingeLoss)

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Type Parameters

TKernel
TInput

The AveragedStochasticGradientDescentTKernel, TInput type exposes the following members.

Constructors

	Name	Description
	AveragedStochasticGradientDescentTKernel, TInput	Initializes a new instance of the AveragedStochasticGradientDescentTKernel, TInput class

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Properties

	Name	Description
	CurrentEpoch	Gets or sets the current epoch counter. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Iterations	Obsolete. Please use MaxIterations instead. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Kernel	Gets or sets the kernel function use to create a kernel Support Vector Machine. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Lambda	Gets or sets the lambda regularization term. Default is 0.5. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	LearningRate	Gets or sets the learning rate for the SGD algorithm. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Loss	Gets or sets the loss function to be used. Default is to use the LogisticLoss. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	MaxIterations	Gets or sets the number of iterations that should be performed by the algorithm when calling Learn(TInput, Boolean, Double). Default is 0 (iterate until convergence). (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Model	Gets or sets the classifier being learned. (Inherited from BinaryLearningBaseTModel, TInput.)
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Tolerance	Gets or sets the maximum relative change in the watched value after an iteration of the algorithm used to detect convergence. Default is 1e-3. If set to 0, the loss will not be computed during learning and execution will be faster. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)

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Methods

	Name	Description
	Clone	Creates a new object that is a copy of the current instance. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Learn(TInput, Boolean, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Double, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Boolean, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BaseAveragedStochasticGradientDescentTModel, TKernel, TInput, TLoss.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Reference

Accord.MachineLearning.VectorMachines.Learning Namespace

AveragedStochasticGradientDescentAddLanguageSpecificTextSet("LSTF698DA30_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TKernel, TInputAddLanguageSpecificTextSet("LSTF698DA30_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

AveragedStochasticGradientDescentTKernel, TInput Class