evaluate_class_mlp — Calculate the evaluation of a feature vector by a multilayer
perceptron.
evaluate_class_mlp computes the result Result of
evaluating the feature vector Features with the multilayer
perceptron (MLP) MLPHandle. The formulas used for the
evaluation are described with create_class_mlp. Before
calling evaluate_class_mlp, the MLP must be trained with
train_class_mlp.
If the MLP is used for regression (function approximation), i.e., if
(OutputFunction = 'linear'), Result is
the value of the function at the coordinate Features. For
OutputFunction = 'logistic' and
'softmax', the values in Result can be interpreted
as probabilities. Hence, for OutputFunction =
'logistic' the elements of Result represent the
probabilities of the presence of the respective independent
attributes. Typically, a threshold of 0.5 is used to decide whether
the attribute is present or not. Depending on the application,
other thresholds may be used as well. For OutputFunction
= 'softmax' usually the position of the maximum value of
Result is interpreted as the class of the feature vector,
and the corresponding value as the probability of the class. In
this case, classify_class_mlp should be used instead of
evaluate_class_mlp because classify_class_mlp
directly returns the class and corresponding probability.
MLPHandle (input_control) class_mlp → (handle)
MLP handle.
Features (input_control) real-array → (real)
Feature vector.
Result (output_control) real-array → (real)
Result of evaluating the feature vector with the MLP.
If the parameters are valid, the operator evaluate_class_mlp
returns the value 2 (H_MSG_TRUE). If necessary, an exception is
raised.
train_class_mlp,
read_class_mlp
Christopher M. Bishop: “Neural Networks for Pattern Recognition”;
Oxford University Press, Oxford; 1995.
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London;
1999.
Foundation