A new combined KSVM and KFD model for classification and recognition
Bibliographic record
Abstract
The Kernel Support Vector Machine (KSVM) is a powerful nonlinear classification methodology where, the Support Vectors (SVs) fully describe the decision surface by incorporating local information in the Kernel space. On the other hand, the Kernel Fisher Discriminant(KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. This paper proposes a novel KSVM + KFD model which combines these two methods. This model can be viewed as an extension to the KSVM by incorporating 'global' characteristics of the data to estimate the decision boundary in the Kernel space. On the other hand, this new model could also be considered as an improvement to the KFD by incorporating the Support Vectors (local margin concept) into the KFD formulation. The KSVM + KFD model can be reduced to the classical KSVM model so that existing KSVM software can be used for easy implementation. An extensive comparison of the KSVM + KFD to the KFD, KSVM, Linear Discriminant Analysis (LDA), Linear Support Vector Machine (LSVM) and the combined LSVM and LDA, performed on real data sets, has shown the advantages of our proposed model. In particular, the experiments on face recognition have clearly shown the superiority of the KSVM + KFD over other methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".