Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and Kth Nearest Neighbours
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.
Bibliographic record
Abstract
The principal objective of this work is to demonstrate efficient parameter selection for various networks used in binary image segmentation. The Support Vector Machines using four kernel functions (i.e., Radial Basis Function, Quadratic, Polynomial, and Linear), Neural Networks (i.e., Feed-forward Back-propagation) and K th Nearest Neighbours algorithm were applied to five different datasets that had been generated from a given image. Pixel coordinates (x,y) were considered as inputs. Grid search and cross-validation were performed to identify the optimal network parameters. All experiments were repeated five times in order to develop confidence in the obtained results. High accuracy was achieved in most cases 95% for SVM-RBF, 90.4% for SVM-Quadratic, 90.8% for SVM-Polynomial, 60% for SVM-Linear, 88% for Neural Networks and 97% for K-NN. After grid search for SVM-RBF, the accuracy reached 98%. In this project, SVM-RBF showed a high level of accuracy and consistency. It was also found that the selected features (pixel coordinates) were discriminative.
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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 | 0.004 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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