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Showing content from http://accord-framework.net/docs/html/T_Accord_MachineLearning_IMultilabelClassifier_2.htm below:

IMultilabelClassifierTInput, TClasses Interface

Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.

Namespace:  Accord.MachineLearning
Assembly:

Accord (in Accord.dll) Version: 3.8.0

Syntax 
public interface IMultilabelClassifier<in TInput, TClasses> : IClassifier<TInput, TClasses>, 
	IClassifier, ITransform<TInput, TClasses>, ICovariantTransform<TInput, TClasses>, 
	ITransform

Public Interface IMultilabelClassifier(Of In TInput, TClasses)
	Inherits IClassifier(Of TInput, TClasses), IClassifier, 
	ITransform(Of TInput, TClasses), ICovariantTransform(Of TInput, TClasses), 
	ITransform
Request Example View Source Type Parameters
TInput
The data type for the input data. Default is double[].
TClasses
The data type for the class labels. Default is int[].

The IMultilabelClassifierTInput, TClasses type exposes the following members.

Properties Methods   Name Description Decide(TInput)

Computes a class-label decision for a given input.

(Inherited from IClassifierTInput, TClasses.) Decide(TInput)

Computes class-label decisions for each vector in the given input.

(Inherited from IClassifierTInput, TClasses.) Decide(TInput, TClasses)

Computes class-label decisions for each vector in the given input.

(Inherited from IClassifierTInput, TClasses.) Decide(TInput, TClasses)

Computes class-label decisions for the given input.

Transform(TInput)

Applies the transformation to an input, producing an associated output.

(Inherited from ICovariantTransformTInput, TOutput.) Transform(TInput)

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

(Inherited from ICovariantTransformTInput, TOutput.) Transform(TInput, TOutput)

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

(Inherited from ITransformTInput, TOutput.) Top See Also

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