BaseSupportVectorRegressionTModel, TKernel, TInput Class

Base class for SupportVectorMachine regression learning algorithms.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorRegressionTModel, TKernel, TInput
    Accord.MachineLearning.VectorMachines.LearningBaseFanChenLinSupportVectorRegressionTModel, TKernel, TInput
    Accord.MachineLearning.VectorMachines.LearningBaseLinearRegressionCoordinateDescentTModel, TKernel, TInput
    Accord.MachineLearning.VectorMachines.LearningBaseLinearRegressionNewtonMethodTModel, TKernel, TInput
    Accord.MachineLearning.VectorMachines.LearningBaseSequentialMinimalOptimizationRegressionTModel, TKernel, TInput

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

Syntax

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public abstract class BaseSupportVectorRegression<TModel, TKernel, TInput> : ISupervisedLearning<TModel, TInput, double>
where TModel : SupportVectorMachine<TKernel, TInput>, ISupportVectorMachine<TInput>
where TKernel : Object, IKernel<TInput>
where TInput : ICloneable

Public MustInherit Class BaseSupportVectorRegression(Of TModel As {SupportVectorMachine(Of TKernel, TInput), ISupportVectorMachine(Of TInput)}, TKernel As {Object, IKernel(Of TInput)}, TInput As ICloneable)
	Implements ISupervisedLearning(Of TModel, TInput, Double)

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Type Parameters

TModel
TKernel
TInput

The BaseSupportVectorRegressionTModel, TKernel, TInput type exposes the following members.

Constructors

	Name	Description
	BaseSupportVectorRegressionTModel, TKernel, TInput	Initializes a new instance of the BaseSupportVectorRegressionTModel, TKernel, TInput class.
	BaseSupportVectorRegressionTModel, TKernel, TInput(ISupportVectorMachineTInput, TInput, Double)	Obsolete.

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Properties

	Name	Description
	C	Gets or sets the cost values associated with each input vector.
	Complexity	Complexity (cost) parameter C. Increasing the value of C forces the creation of a more accurate model that may not generalize well. If this value is not set and UseComplexityHeuristic is set to true, the framework will automatically guess a value for C. If this value is manually set to something else, then UseComplexityHeuristic will be automatically disabled and the given value will be used instead.
	Epsilon	Insensitivity zone ε. Increasing the value of ε can result in fewer support vectors in the created model. Default value is 1e-3.
	Inputs	Gets or sets the input vectors for training.
	IsLinear	Gets whether the machine to be learned has a Linear kernel.
	Kernel	Gets or sets the kernel function use to create a kernel Support Vector Machine. If this property is set, UseKernelEstimation will be set to false.
	Model	Gets the machine to be taught.
	Outputs	Gets or sets the output values for each calibration vector.
	Token	Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.
	UseComplexityHeuristic	Gets or sets a value indicating whether the Complexity parameter C should be computed automatically by employing an heuristic rule. Default is false.
	UseKernelEstimation	Gets or sets whether initial values for some kernel parameters should be estimated from the data, if possible. Default is true.
	Weights	Gets or sets the individual weight of each sample in the training set. If set to null, all samples will be assumed equal weight. Default is null.

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Methods

	Name	Description
	ComputeError	Obsolete. Obsolete.
	Create	Creates an instance of the model to be learned. Inheritors of this abstract class must define this method so new models can be created from the training data.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InnerRun	Runs the learning algorithm.
	Learn	Learns a model that can map the given inputs to the given outputs.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Run	Obsolete. Obsolete.
	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

BaseSupportVectorRegressionAddLanguageSpecificTextSet("LSTEDB37402_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TKernel, TInputAddLanguageSpecificTextSet("LSTEDB37402_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseSupportVectorRegressionTModel, TKernel, TInput Class