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Shouldn't one just use a naive bayesian classifier on top of the frequency-weighted language keywords? The data would then really only be needed to guess the f
by dmichulke 10y ago
Shouldn't one just use a naive bayesian classifier on top of the frequency-weighted language keywords?
The data would then really only be needed to guess the frequencies.
I am also not sure whether word bigrams would help a lot, but character level n-grams would be totally off my radar.
Any opinions on that?
- pprogrammer 10y agoThat would probably work OK, I guess, but some languages have the same keywords, so I don't think you'll get very high accuracy. I also tried a word level n-grams with linear classifiers, I couldn't get very high accuracy out of it. Also n-grams with higher values for n improve accuracy a lot if you train it with a lot of data, both for word-level models as for character level.