A NN Image Classification Method Driven by the Mixed Fitness Function
Bibliographic record
Abstract
The mixed fitness function of the error sum squares linear transformation is proposed in the article, and this function can improve the evaluation method of the individual fitness, and combining with NN, this method can be used in the high-speed paper money image analysis system. Aiming at many characters such as the high comparability of paper money images of different denominations, small class distance and large in-class discreteness induced by the using abrasion, this method first codes the weight values and threshold values of NN with real values, and transforms the problem from the representation type to the genotype, and performs many genetic operations such as selecting, crossing and variation, and takes the weight value and threshold value trained by the genetic algorithm according to the individual fitness value of the mixed fitness function as the initial weight value and initial threshold value of NN in the next stage, and trains these values by NN to establish the sorter. This method was tested in the embedded system with resource restriction (TI TMS320C6713 DSP), and 20000 RMB images were acquired as the samples, and 12000 images of them were tested, and the test result indicated that the method combining improved genetic algorithm with NN obviously enhanced the recognition rate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".