LP SVM with A Novel Similarity function Outperforms Powerful LP-QP-Kernel-SVM Considering Efficient Classification
Bibliographic record
Abstract
Abstract– While the core quality of SVM comes from its ability to get the global optima, its classification performance also depends on computing kernels.However, while this kernel-complexity generates the power of machine, it is also responsible for the compu-tational load to execute this kernel. Moreover, insisting on a similarity function to be a positive definite kernel demands some properties to be satisfied that seem unproductive sometimes raising a question about which similarity measures to be used for classifier. We model Vapnik’s LPSVM proposing a new similarity function replacing kernel func-tion.Following the strategy of ”Accuracy first, speed second”, we have modelled a similarity function that is mathematically well-defined depending on analysis as well as geometry and complex enough to train the machine for generating solid generalization ability. Being consistent with the theory of learning by Balcan and Blum [1], our similarity function does not need to be a valid kernel function and demands less computational cost for executing compared to its counterpart like RBF or other kernels while provides sufficient power to the classifier using its optimal complexity. Benchmarking shows that our similarity function based LPSVM poses test error 0.86 times of the most powerful RBF based QP SVM but demands only 0.40 times of its computational cost.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".