IAPR keynote lecture III: Methods of achieving perfect recognition scores
Bibliographic record
Abstract
Recognition systems inevitably make some errors somewhere at some time. Achieving perfect recognition without making errors has been the dream of researchers in the field of pattern recognition. This talk summarizes my efforts and experiences towards this goal. The first part of this talk will describe my early efforts in building different types of classifiers based on structural analyses and skeletonization, density distributions, neural networks, tree hierarchies, support vectors, and so on. To improve the recognition rates further, multiple classifiers were explored involving numerous types of geometric and structural features, and ensembles of hybrid classifiers. Later, error reduction machines were introduced and investigated. Several effective ways of heading towards perfect scores will be presented with real-life examples and promising research results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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 source (direct Gemma or distilled Codex), 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".