Some new algorithms and software implementation methods for pattern recognition research
Bibliographic record
Abstract
This paper, in two parts, describes some novel algorithms for pattern recognition research and a framework for efficient development, maintenance, and sharing of Interactive software amongst several users and diverse application areas. This modular interactive software system (MISS) forms the basis of a general purpose image analysis and pattern recognition research system (IPS) implemented in the Macdonald Stewart Biomedical Image Processing Laboratory at McGill University. The first part of the paper discusses the algorithms and some preliminary results. Two algorithms are singled out. The first is an interactive approach to nouparametric feature selection via two-dimensional mapping of the multidimension al minimal spanning tree of the features in pattern space. Some preliminary results of the performance of the algorithm, in the automatic mode, applied to feature selection for cervical cell classification, are presented. The second algorithm is an exact procedure for condensing the training data, in the nearest neighbor decision rule, which yields a minimal set of points that implements precisely the original nearest neighbor decision boundary. The second part of the paper describes the MISS and IPS software systems. The MISS software implementation framework insures software colbpati bility and sharing among many individuals and diverse applications, provides safeguard against software loss, and supports an extendable high level interactive language with on-line document ation. MISS language support includes a BASE LAN GUAGE interpreter (implementing a variant of FORTRAN) plus an EXTENDED LANGUAGE interpreter that facilitates addition of new groups of language statements. Each group of statements is associated with a particular function, application area, or programmer. A-11 IPS software has been implemented within the MISS framework. The present IPS implementation includes over 300 EXTENDED LANGUAGE statements in twenty groups facilitating such functions as: image acquisition and display, simulation of a hardware image processor, data management, image manipulation and filtering, graphics, image segmentation and feature extraction, feature selection, classification, and classification per formance measurement. The overall design philosophy of the MISS and IPS software systems and the ease with which new software can be added and documented are described.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".