OneVsOneLearningTBinary, TModel Class

Base learning algorithm for OneVsOneTBinary, TInput multi-class classifiers.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearningOneVsOneLearningDouble, TBinary, TModel
      Accord.MachineLearningOneVsOneLearningTBinary, TModel
        Accord.MachineLearning.VectorMachines.LearningBaseMulticlassSupportVectorLearningTBinary, TKernel, TModel

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

Syntax

Copy

public abstract class OneVsOneLearning<TBinary, TModel> : OneVsOneLearning<double[], TBinary, TModel>
where TBinary : class, Object, IBinaryClassifier<double[]>, ICloneable
where TModel : OneVsOne<TBinary>

Public MustInherit Class OneVsOneLearning(Of TBinary As {Class, Object, IBinaryClassifier(Of Double()), ICloneable}, TModel As OneVsOne(Of TBinary))
	Inherits OneVsOneLearning(Of Double(), TBinary, TModel)

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Type Parameters

TBinary: The type for the inner binary classifiers used in the one-vs-one approach.
TModel: The type of the model being learned.

The OneVsOneLearningTBinary, TModel type exposes the following members.

Constructors

	Name	Description
	OneVsOneLearningTBinary, TModel	Initializes a new instance of the OneVsOneLearningTBinary, 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 OneVsOneLearningTInput, TBinary, TModel.)
	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-one classification strategy. (Inherited from OneVsOneLearningTInput, TBinary, TModel.)
	Model	Gets or sets the model being learned. (Inherited from OneVsOneLearningTInput, 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 OneVsOneLearningTInput, 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 OneVsOneLearningTInput, 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 OneVsOneLearningTInput, 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	Learns a model that can map the given inputs to the given outputs. (Inherited from OneVsOneLearningTInput, TBinary, TModel.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	OnSubproblemFinished	Raises the [E:SubproblemFinished] event. (Inherited from OneVsOneLearningTInput, TBinary, TModel.)
	OnSubproblemStarted	Raises the [E:SubproblemStarted] event. (Inherited from OneVsOneLearningTInput, 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 OneVsOneLearningTInput, TBinary, TModel.)
	SubproblemStarted	Occurs when the learning of a subproblem has started. (Inherited from OneVsOneLearningTInput, 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 Namespace

Accord.MachineLearning.VectorMachines.LearningMulticlassSupportVectorLearningTKernel

OneVsOneLearningAddLanguageSpecificTextSet("LST25E83A2E_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TBinary, TModelAddLanguageSpecificTextSet("LST25E83A2E_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

OneVsOneLearningTBinary, TModel Class