A New Clustering Method for Improving Plasticity and Stability in Handwritten Character Recognition Systems
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Bibliographic record
Abstract
This paper presents a new online clustering algorithm in order to improve plasticity and stability in handwritten character recognition systems. Our clustering algorithm is able to automatically determine the optimal number of clusters in the input data. An incremental learning technique similar to adaptive resonance theory (ART) is used to determine the best cluster for new data. Our technique also allows the previously learned clusters to be merged whenever the newly arrived data points push their centers close together. We also developed new features and similarity measures in order to describe and compare the shapes of handwritten digits to be used in our clustering algorithm. Results of our algorithm on clustering the shapes of the handwritten numerals from the CENPARMI isolated digit database are shown. Our method can incrementally learn new handwriting styles of digits, without forgetting the previous ones, therefore it can improve plasticity and stability
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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 | 0.000 |
| 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 it