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Hi Leo,
as mentioned some ideas for a later pull request to add a tuning wrapper to rslocal.
This is what such a tuner might tackle:
If new sets of samples are predicted, best configuration of the three parameters is empirically evaluated. The user would therefore be able to provide combinations of those. (i.e., full-factorial or predefined combinations).
Besides heuristic parameter optimization, such a wrapper could also provide clustering to narrow down exhaustive search of a spectral library.
Such a tuner might also implement early stopping to make search more efficient.
Is there anything else to consider for such a functionality? I hope to soon find some time to contribute with some code to achive this.
Cheers,
Philipp
The text was updated successfully, but these errors were encountered:
Hi Leo,
as mentioned some ideas for a later pull request to add a tuning wrapper to
rslocal
.This is what such a tuner might tackle:
If new sets of samples are predicted, best configuration of the three parameters is empirically evaluated. The user would therefore be able to provide combinations of those. (i.e., full-factorial or predefined combinations).
Besides heuristic parameter optimization, such a wrapper could also provide clustering to narrow down exhaustive search of a spectral library.
Such a tuner might also implement early stopping to make search more efficient.
Is there anything else to consider for such a functionality? I hope to soon find some time to contribute with some code to achive this.
Cheers,
Philipp
The text was updated successfully, but these errors were encountered: