learn_sampset2 ( : : SampKey, Outfile, NSamples, StopError, ErrorN : )

Training of the classificator with one data set.

learn_sampset2 trains the current classificator with data for the key SampKey (see read_sampset). The training sequence is terminated at least after NSamples examples. If NSamples is bigger than the number of examples in SampKey, then a cyclic start at the beginning occurs. If the error underpasses the value StopError, then the training sequence is terminated in advance. StopError is calculated with N / ErrorN. Whereby N significates the number of examples which were classified wrong during the last ErrorN training examples. Typically ErrorN is the number of examples in SampKey and NSamples is a multiple of it. If you want a data set with 100 examples to run 5 times at most and if you want it to terminate with an error lower than 5, then the corresponding values are NSamples = 500, ErrorN = 100 and StopError = 0.05. A protocol of the training activity is going to be written in file Outfile.


Parameters

SampKey (input_control)
integer -> integer
Number of the data set to train.

Outfile (input_control)
filename.named -> string
Name of the protocol file.
Default value: 'training_prot'

NSamples (input_control)
integer -> integer
Number of arrays of attributes to learn.
Default value: 500

StopError (input_control)
real -> real
Classification error for termination.
Default value: 0.05

ErrorN (input_control)
integer -> integer
Error during the assignment.
Default value: 100


Result

learn_sampset2 returns TRUE. An exception handling is raised, if there is no key SampKey or if there are problems while opening the file.


Possible Predecessors

create_classif2, set_classif2


Possible Successors

test_sampset2, enquire_classif2, write_classif2, close_classif2, free_sampset


See also

test_sampset2, enquire_classif2, learn_classif2, read_sampset



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