ClassesClasses | | Operators

classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvm (Operator)

Name

classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvm — Classify a feature vector by a support vector machine.

Signature

classify_class_svm( : : SVMHandle, Features, Num : Class)

Herror T_classify_class_svm(const Htuple SVMHandle, const Htuple Features, const Htuple Num, Htuple* Class)

void ClassifyClassSvm(const HTuple& SVMHandle, const HTuple& Features, const HTuple& Num, HTuple* Class)

HTuple HClassSvm::ClassifyClassSvm(const HTuple& Features, const HTuple& Num) const

static void HOperatorSet.ClassifyClassSvm(HTuple SVMHandle, HTuple features, HTuple num, out HTuple classVal)

HTuple HClassSvm.ClassifyClassSvm(HTuple features, HTuple num)

Description

classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvm computes the best NumNumNumNumnum classes of the feature vector FeaturesFeaturesFeaturesFeaturesfeatures with the SVM SVMHandleSVMHandleSVMHandleSVMHandleSVMHandle and returns them in ClassClassClassClassclassVal. If the classifier was created in the Mode = 'one-versus-one'"one-versus-one""one-versus-one""one-versus-one""one-versus-one", the classes are ordered by the number of votes of the sub-classifiers. If Mode = 'one-versus-all'"one-versus-all""one-versus-all""one-versus-all""one-versus-all" was used, the classes are ordered by the value of each sub-classifier (see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvm for more details). If the classifier was created in the Mode = 'novelty-detection'"novelty-detection""novelty-detection""novelty-detection""novelty-detection", it determines whether the feature vector belongs to the same class as the training data (ClassClassClassClassclassVal = 1) or is regarded as outlier (ClassClassClassClassclassVal = 0). In this case NumNumNumNumnum must be set to 1 as the classifier only determines membership.

Before calling classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvm, the SVM must be trained with train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvm.

Execution Information

Parameters

SVMHandleSVMHandleSVMHandleSVMHandleSVMHandle (input_control)  class_svm HClassSvm, HTupleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

SVM handle.

FeaturesFeaturesFeaturesFeaturesfeatures (input_control)  real-array HTupleHTupleHtuple (real) (double) (double) (double)

Feature vector.

NumNumNumNumnum (input_control)  integer-array HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of best classes to determine.

Default value: 1

Suggested values: 1, 2, 3, 4, 5

ClassClassClassClassclassVal (output_control)  integer(-array) HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Result of classifying the feature vector with the SVM.

Result

If the parameters are valid the operator classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Possible Predecessors

train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvm, read_class_svmread_class_svmReadClassSvmReadClassSvmReadClassSvm

Alternatives

apply_dl_classifierapply_dl_classifierApplyDlClassifierApplyDlClassifierApplyDlClassifier

See also

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvm

References

John Shawe-Taylor, Nello Cristianini: “Kernel Methods for Pattern Analysis”; Cambridge University Press, Cambridge; 2004.
Bernhard Schölkopf, Alexander J.Smola: “Learning with Kernels”; MIT Press, London; 1999.

Module

Foundation


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