Wavelet packet best basis search using generalized Renyi entropy
Bibliographic record
Abstract
This paper introduces an approach to wavelet packet best basis searches using the generalized Renyi entropy. The approach extends work by R.R. Coifman and M.V. Wickerhauser who showed how Shannon entropy can be used as an additive cost function in the wavelet packet best basis selection (see IEEE Trans. on Inform. Theory, vol.38, no.2, p.713-18, 1992). This paper also extends the idea of an additive cost function to an arithmetic mean. These extensions allow for a redefinition of additive cost functions as arithmetic means in a way consistent with the approach of Coifman and Wickerhauser. The approach using an arithmetic mean is then extended to include the geometric mean. This extension to geometric means allows us to introduce the Renyi generalized entropy as a cost function in the best basis search. These two extensions also allow the use of incomplete probability distributions, whereas Coifman and Wickerhauser's entropy based cost function is limited to complete probability distributions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".