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nowadays, docs with rarely interpretation is difficult to understand the algorithm,
Such as bayes rule sets and other Underdogs have little references.
If can provide a common introduce in interface level may be good for programmers
who have little information of algorithm to start
The text was updated successfully, but these errors were encountered:
For example, algorithm in aad dir are almost classical algorithm with rule based listener. I have some understand of basic anomaly detection algorithms in project: https://github.com/google/active-learning
These algorithms are too simple without ensemble, so the keypoint I want to understand is the
algorithm construction "refinement" used in aad dir and the "loss" construction without dive into
"Active Anomaly Detection via Ensembles"
In the common sense, ensemble is some sense of "voting", but it seemed like your "voting" is with some "prior", Do you have a understandable interpretation ?
I appreciate the feedback. The code was originally developed for faster iterations during research and hence the documentation exists mostly in the papers. I will try to address this important issue in the near future by adding more comments to the major APIs.
nowadays, docs with rarely interpretation is difficult to understand the algorithm,
Such as bayes rule sets and other Underdogs have little references.
If can provide a common introduce in interface level may be good for programmers
who have little information of algorithm to start
The text was updated successfully, but these errors were encountered: