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Record W2125381833 · doi:10.1109/cmpsac.1979.762465

Some new algorithms and software implementation methods for pattern recognition research

2005· article· en· W2125381833 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning and Data Classification
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceSoftwareInterpreterSoftware systemAlgorithmSoftware developmentData miningArtificial intelligenceMachine learningProgramming language

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.994
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.176
GPT teacher head0.499
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations23
Published2005
Admission routes2
Has abstractyes

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