sylseg-sk-training(1) USER COMMANDS sylseg-sk-training(1)NAME
sylseg-sk-training - train the statistic for the syllabic segmentation
SYNOPSIS
sylseg-sk-training [--color] [--dl debug level] [--help] [<input file>]
DESCRIPTION
The sylabic segmentation is esential for some linguistic or speech recognition applications. Depending on the language either rule based or
statistical approach is beying used. For Slovak the statistical approach seems to be more suitable.
sylseg-sk-training creates the necessary data for one of the statistical approaches for the syllabic segmentaion. The input data are
expected to be word segmented into syllables. The syllabic separator should be "-". If no input file is specified, the standard input is
expected. The output is written in to the file. The filename is input filename with the extension ".statistics". If standard input is
used, then the output filename is syl_output.statistics.
The design of the sylseg-sk-training is language independent. Theoretically it should work for any language.
OPTIONS --color
Enable color output.
--dl Set the debug level. Control the amount of displayed information The debug level 0 displays nothing. The maximum level 5 displays
full debugging report. The default debug level is 1.
--help display a short help text
EXAMPLES
Use file aaa.txt as training input and set up debug level to 3:
sylseg-sk-training --dl 3 aaa.txt
EXIT STATUS
sylseg-sk-training returns a zero if the training succeeds
AUTHOR
Jozef Ivanecky (dodo (at) kanoistika.sk)
SEE ALSO sylseg-sk(1)version 0.4 September 9, 2006 sylseg-sk-training(1)
Check Out this Related Man Page
FANN_CASCADETRAIN_ON_DATA(3) 1 FANN_CASCADETRAIN_ON_DATA(3)fann_cascadetrain_on_data - Trains on an entire dataset, for a period of time using the Cascade2 training algorithmSYNOPSIS
bool fann_cascadetrain_on_data (resource $ann, resource $data, int $max_neurons, int $neurons_between_reports, float $desired_error)
DESCRIPTION
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE(3) value should change
within fann_get_cascade_output_stagnation_epochs(3) during training of the output connections, in order for the training not to stagnate.
If the training stagnates, the training of the output connections will be ended and new candidates will be prepared.
This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm as it's internal train-
ing algorithm. This algorithm can be set to either FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by fann_set_training_algorithm(3), and the
parameters set for these training algorithms will also affect the cascade training.
PARAMETERS
o $ann
-Neural network resource.
o $data
-Neural network training data resource.
o $max_neurons
- The maximum number of neurons to be added to neural network.
o $neurons_between_reports
- The number of neurons between printing a status report. A value of zero means no reports should be printed.
o $desired_error
- The desired fann_get_MSE(3) or fann_get_bit_fail(3), depending on which stop function is chosen by fann_set_train_stop_func-
tion(3)RETURN VALUES
Returns TRUE on success, or FALSE otherwise.
SEE ALSO fann_train_on_data(3), fann_cascadetrain_on_file(3).
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