Local feature based pattern classification: from principle to application
Bibliographic record
Abstract
This thesis demonstrates that local feature based approaches are always more stable than global feature based approaches for pattern classification problems. Guided by the original theory that a regional matching approach is more robust than a national matching approach for two-dimensional pattern classification, this thesis examines the applications of the theory in one-dimensional and two-dimensional pattern classifications. We propose two local feature based approaches for two significant applications of pattern classification, namely start codon prediction and content based image classification. For start codon prediction which is considered as a typical one-dimensional pattern classification problem, we have developed a districted neural network that can be taken as a regional voting version of the conventional neural network. Experiments have been performed on the well known translation initiation sites (TIS) data sets and results have shown significant improvement of prediction accuracy. For two-dimensional pattern classification, we propose differential latent semantic index (DLSI) approach for content based image classification. The feasibility of using local features in the DLSI method is also investigated and an extensive experimental study on a real image database has proved its effectiveness.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".