Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications
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Bibliographic record
Abstract
Hidden Markov Models (HMMs) have been shown to be useful in handwritten pattern recognition. However, owing to their fundamental structure, they have little resistance to unexpected noise among observation sequences. In other words, unexpected noise in a sequence might "break" the normal transmission of states for this sequence, making it unrecognizable to trained models. To resolve this problem, we propose a leave-one-out-training strategy, which will make the models more robust. We also propose a leave-one-out-testing method, which will compensate for some of the negative effects of this noise. The latter is actually an example of a system with a single classifier and multiple classifications. Compared with the 98.00 percent accuracy of the benchmark HMMs, the new system achieves a 98.88 percent accuracy rate on handwritten digits.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 it