BaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel Class

Base class for multi-label support vector learning algorithms.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearningOneVsRestLearningTInput, TBinary, TModel
      Accord.MachineLearning.VectorMachines.LearningBaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel
        Accord.MachineLearning.VectorMachines.LearningMultilabelSupportVectorLearning
        Accord.MachineLearning.VectorMachines.LearningMultilabelSupportVectorLearningTKernel
        Accord.MachineLearning.VectorMachines.LearningMultilabelSupportVectorLearningTKernel, 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 BaseMultilabelSupportVectorLearning<TInput, TBinary, TKernel, TModel> : OneVsRestLearning<TInput, TBinary, TModel>
where TInput : ICloneable
where TBinary : SupportVectorMachine<TKernel, TInput>
where TKernel : Object, IKernel<TInput>
where TModel : OneVsRest<TBinary, TInput>

Public MustInherit Class BaseMultilabelSupportVectorLearning(Of TInput As ICloneable, TBinary As SupportVectorMachine(Of TKernel, TInput), TKernel As {Object, IKernel(Of TInput)}, TModel As OneVsRest(Of TBinary, TInput))
	Inherits OneVsRestLearning(Of TInput, TBinary, TModel)

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Type Parameters

TInput
TBinary
TKernel
TModel

The BaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel type exposes the following members.

Constructors

	Name	Description
	BaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel	Initializes a new instance of the BaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel class

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Properties

	Name	Description
	AggregateExceptions	Gets or sets a value indicating whether the entire training algorithm should stop in case an exception has been detected at just one of the inner binary learning problems. Default is true (execution will not be stopped). (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	IsMultilabel	Gets or sets a value indicating whether the learning algorithm should generate multi-label (as opposed to multi-class) models. If left unspecified, the type of the model will be determined automatically depending on which overload of the Learn(TInput, Boolean, Double) method will be called first by the executing code. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	Kernel	Gets or sets the kernel function to be used to learn the kernel support vector machines.
	Learner	Gets or sets a function that takes a set of parameters and creates a learning algorithm for learning each of the binary inner classifiers needed by the one-vs-rest classification strategy. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	Model	Gets or sets the model being learned. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)

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Methods

	Name	Description
	ConfigureTResult(FuncTResult)	Sets a callback function that takes a set of parameters and creates a learning algorithm for learning each of the binary inner classifiers needed by the one-vs-rest classification strategy. Calling this method sets the Learner property. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	ConfigureT, TResult(FuncT, TResult)	Sets a callback function that takes a set of parameters and creates a learning algorithm for learning each of the binary inner classifiers needed by the one-vs-rest classification strategy. Calling this method sets the Learner property. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	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. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	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.)
	Learn(TInput, Boolean, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	OnSubproblemFinished	Raises the [E:SubproblemFinished] event. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	OnSubproblemStarted	Raises the [E:SubproblemStarted] event. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Events

	Name	Description
	SubproblemFinished	Occurs when the learning of a subproblem has finished. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)
	SubproblemStarted	Occurs when the learning of a subproblem has started. (Inherited from OneVsRestLearningTInput, TBinary, TModel.)

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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

BaseMultilabelSupportVectorLearningAddLanguageSpecificTextSet("LST42486E28_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TInput, TBinary, TKernel, TModelAddLanguageSpecificTextSet("LST42486E28_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel Class