ClassesClassesClassesClasses | | | | Operators

classify_class_mlpT_classify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp (Operator)

Name

classify_class_mlpT_classify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp — Calculate the class of a feature vector by a multilayer perceptron.

Signature

classify_class_mlp( : : MLPHandle, Features, Num : Class, Confidence)

Herror T_classify_class_mlp(const Htuple MLPHandle, const Htuple Features, const Htuple Num, Htuple* Class, Htuple* Confidence)

Herror classify_class_mlp(const HTuple& MLPHandle, const HTuple& Features, const HTuple& Num, Hlong* Class, double* Confidence)

Herror classify_class_mlp(const HTuple& MLPHandle, const HTuple& Features, const HTuple& Num, HTuple* Class, HTuple* Confidence)

HTuple HClassMlp::ClassifyClassMlp(const HTuple& Features, const HTuple& Num, HTuple* Confidence) const

void ClassifyClassMlp(const HTuple& MLPHandle, const HTuple& Features, const HTuple& Num, HTuple* Class, HTuple* Confidence)

HTuple HClassMlp::ClassifyClassMlp(const HTuple& Features, const HTuple& Num, HTuple* Confidence) const

Hlong HClassMlp::ClassifyClassMlp(const HTuple& Features, const HTuple& Num, double* Confidence) const

void HOperatorSetX.ClassifyClassMlp(
[in] VARIANT MLPHandle, [in] VARIANT Features, [in] VARIANT Num, [out] VARIANT* Class, [out] VARIANT* Confidence)

VARIANT HClassMlpX.ClassifyClassMlp(
[in] VARIANT Features, [in] VARIANT Num, [out] VARIANT* Confidence)

static void HOperatorSet.ClassifyClassMlp(HTuple MLPHandle, HTuple features, HTuple num, out HTuple classVal, out HTuple confidence)

HTuple HClassMlp.ClassifyClassMlp(HTuple features, HTuple num, out HTuple confidence)

int HClassMlp.ClassifyClassMlp(HTuple features, HTuple num, out double confidence)

Description

classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp computes the best NumNumNumNumNumnum classes of the feature vector FeaturesFeaturesFeaturesFeaturesFeaturesfeatures with the multilayer perceptron (MLP) MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle and returns the classes in ClassClassClassClassClassclassVal and the corresponding confidences (probabilities) of the classes in ConfidenceConfidenceConfidenceConfidenceConfidenceconfidence. Before calling classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp, the MLP must be trained with train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp.

classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp can only be called if the MLP is used as a classifier with OutputFunction = 'softmax'"softmax""softmax""softmax""softmax""softmax" (see create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp). Otherwise, an error message is returned. classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp corresponds to a call to evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp and an additional step that extracts the best NumNumNumNumNumnum classes. As described with evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp, the output values of the MLP can be interpreted as probabilities of the occurrence of the respective classes. In most cases it should be sufficient to use NumNumNumNumNumnum = 1 in order to decide whether the probability of the best class is high enough. In some applications it may be interesting to also take the second best class into account (NumNumNumNumNumnum = 2), particularly if it can be expected that the classes show a significant degree of overlap.

Parallelization

Parameters

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle (input_control)  class_mlp HClassMlp, HTupleHTupleHClassMlp, HTupleHClassMlpX, VARIANTHtuple (integer) (IntPtr) (Hlong) (Hlong) (Hlong) (Hlong)

MLP handle.

FeaturesFeaturesFeaturesFeaturesFeaturesfeatures (input_control)  real-array HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Feature vector.

NumNumNumNumNumnum (input_control)  integer-array HTupleHTupleHTupleVARIANTHtuple (integer) (int / long) (Hlong) (Hlong) (Hlong) (Hlong)

Number of best classes to determine.

Default value: 1

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

ClassClassClassClassClassclassVal (output_control)  integer(-array) HTupleHTupleHTupleVARIANTHtuple (integer) (int / long) (Hlong) (Hlong) (Hlong) (Hlong)

Result of classifying the feature vector with the MLP.

ConfidenceConfidenceConfidenceConfidenceConfidenceconfidence (output_control)  real(-array) HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Confidence(s) of the class(es) of the feature vector.

Result

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

Possible Predecessors

train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp, read_class_mlpread_class_mlpReadClassMlpread_class_mlpReadClassMlpReadClassMlp

Alternatives

evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp

See also

create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp

References

Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London; 1999.

Module

Foundation


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