fredwu/simple_bayes
A Naive Bayes machine learning implementation in Elixir.
Supports three distinct classifier models (multinomial, binarized multinomial, and Bernoulli) with pluggable storage backends—memory, file system, or Erlang's Dets—enabling flexible deployment from in-memory classification to persistent, disk-based learning. Includes preprocessing capabilities like stop-word filtering, additive smoothing, TF-IDF weighting, optional word stemming, and per-training-example keyword weighting for fine-grained model tuning.
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Elixir
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Last pushed
Sep 25, 2017
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