An Incremental Learning Algorithm Based on Weighted Nave Bayes Classification
Why this work is in the frame
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Bibliographic record
Abstract
Nave Bayes(NB)Classifier is a simple and effective classification method based on probability theory.But this algorithm has some lack and limitation,especially the non-maturity training-data collection.Traditional NB classification must costs a lot of time to learn all samples again when new sample added.So a concept of incremental learning algorithm is put forward.The algorithm based on exist classifier,using new information from new sample to modify the classifier.We introduce the main meaning of incremental learning algorithm based on Weighted Nave Bayes.And we give the very algorithm and prove it.Through the analysis of the algorithm,we get the result that Comparing to non-Incremental learning algorithm,the additional space complexity and time complexity of incremental learning algorithm are both in the acceptable range.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it