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HClassKnn (Class)

Name

HClassKnn — Represents an instance of a k-NearestNeighbor classifier.

Constructors

`ReadClassKnn`		Read the k-NN classifier from a file.
`CreateClassKnn`		Create a k-nearest neighbors (k-NN) classifier.

Methods

`AddClassTrainDataKnn`		Add training data to a k-nearest neighbors (k-NN) classifier.
`AddSampleClassKnn`		Add a sample to a k-nearest neighbors (k-NN) classifier.
`AddSamplesImageClassKnn`		Add training samples from an image to the training data of a k-Nearest-Neighbor classifier.
`ClassifyClassKnn`		Search for the next neighbors for a given feature vector.
`ClassifyImageClassKnn`		Classify an image with a k-Nearest-Neighbor classifier.
`ClearClassKnn`		Clear a k-NN classifier.
`CreateClassKnn`		Create a k-nearest neighbors (k-NN) classifier.
`CreateClassLutKnn`		Create a look-up table using a k-nearest neighbors classifier (k-NN) to classify byte images.
`DeserializeClassKnn`		Deserialize a serialized k-NN classifier.
`GetClassTrainDataKnn`		Get the training data of a k-nearest neighbors (k-NN) classifier.
`GetParamsClassKnn`		Get parameters of a k-NN classification.
`GetSampleClassKnn`		Return a training sample from the training data of a k-nearest neighbors (k-NN) classifier.
`GetSampleNumClassKnn`		Return the number of training samples stored in the training data of a k-nearest neighbors (k-NN) classifier.
`ReadClassKnn`		Read the k-NN classifier from a file.
`SelectFeatureSetKnn`		Selects an optimal subset from a set of features to solve a certain classification problem.
`SerializeClassKnn`		Serialize a k-NN classifier.
`SetParamsClassKnn`		Set parameters for k-NN classification.
`TrainClassKnn`		Creates the search trees for a k-NN classifier.
`WriteClassKnn`		Save the k-NN classifier in a file.

HClassKnn (Class)

Name

HClassKnn — Represents an instance of a k-NearestNeighbor classifier.

Constructors

`ReadClassKnn`		Read the k-NN classifier from a file.
`CreateClassKnn`		Create a k-nearest neighbors (k-NN) classifier.

Methods

`AddClassTrainDataKnn`		Add training data to a k-nearest neighbors (k-NN) classifier.
`AddSampleClassKnn`		Add a sample to a k-nearest neighbors (k-NN) classifier.
`AddSamplesImageClassKnn`		Add training samples from an image to the training data of a k-Nearest-Neighbor classifier.
`ClassifyClassKnn`		Search for the next neighbors for a given feature vector.
`ClassifyImageClassKnn`		Classify an image with a k-Nearest-Neighbor classifier.
`ClearClassKnn`		Clear a k-NN classifier.
`CreateClassKnn`		Create a k-nearest neighbors (k-NN) classifier.
`CreateClassLutKnn`		Create a look-up table using a k-nearest neighbors classifier (k-NN) to classify byte images.
`DeserializeClassKnn`		Deserialize a serialized k-NN classifier.
`GetClassTrainDataKnn`		Get the training data of a k-nearest neighbors (k-NN) classifier.
`GetParamsClassKnn`		Get parameters of a k-NN classification.
`GetSampleClassKnn`		Return a training sample from the training data of a k-nearest neighbors (k-NN) classifier.
`GetSampleNumClassKnn`		Return the number of training samples stored in the training data of a k-nearest neighbors (k-NN) classifier.
`ReadClassKnn`		Read the k-NN classifier from a file.
`SelectFeatureSetKnn`		Selects an optimal subset from a set of features to solve a certain classification problem.
`SerializeClassKnn`		Serialize a k-NN classifier.
`SetParamsClassKnn`		Set parameters for k-NN classification.
`TrainClassKnn`		Creates the search trees for a k-NN classifier.
`WriteClassKnn`		Save the k-NN classifier in a file.