create_class_knnT_create_class_knnCreateClassKnnCreateClassKnncreate_class_knn (算子名称)

名称

create_class_knnT_create_class_knnCreateClassKnnCreateClassKnncreate_class_knn — 创建一个K-最近邻(k-NN)分类器。

参数签名

描述

create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn creates a K-最近邻 (k-NN) data structure. This can be either used to classify data or to approximately locate nearest neighbors in a NumDimNumDimNumDimNumDimnumDimnum_dim-dimensional space.

Most of 该算子s described in Classification/K-Nearest-Neighbor use the resulting handle KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleknnhandle.

The k-NN classifies by searching approximately the nearest neighbors and returning their classes as result. With the used approximation, the search time is logarithmically to the number of samples and dimensions.

The dimension of the feature vectors is the only parameter that necessarily has to be set in NumDimNumDimNumDimNumDimnumDimnum_dim.

运行信息

• 多线程类型:可重入(与非独占操作符并行运行)。
• 多线程作用域:全局(可以从任何线程调用)。
• 未经并行化处理。

This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

参数表

结果

如果参数均有效，算子 create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn 返回值 2 ( H_MSG_TRUE) . 如有必要，将引发异常。

可能的后置算子

add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn, train_class_knntrain_class_knnTrainClassKnnTrainClassKnnTrainClassKnntrain_class_knn

可替代算子

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm, create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp

参考其它

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn

References

Marius Muja, David G. Lowe: “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”; International Conference on Computer Vision Theory and Applications (VISAPP 09); 2009.

模块

Foundation

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