Sparse least squares K-SVCR multi-class classification
Bibliographic record
Abstract
This paper introduces a novel model, the sparse least squares K-class support vector classificationregression with adaptive p -norm (PLSTKSVC), to tackle challenges in multi-class classification.Leveraging a "1-versus-1-versus-rest" structure, PLSTKSVC dynamically adjusts the parameter p based on the data, enabling an adaptive learning framework.By incorporating cardinality-constrained optimization, the model seamlessly integrates feature selection and classification.Although the p -norm is non-convex for 0 < p < 1, PLSTKSVC efficiently addresses the associated optimization via linear systems of equations.PLSTKSVC offers several advantages, including simultaneous feature selection and classification, robust theoretical foundations, algorithmic efficiency, and strong empirical validation.The model's theoretical contributions include lower bounds on non-zero solution entries and upper bounds on the optimal solution norm.Experimental results on multi-class classification datasets highlight the superior performance of PLSTKSVC, establishing it as a significant advancement in machine learning.
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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 | 0.001 |
| 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".