Efficient Feature Mapping in Classifying Proportional Data
Bibliographic record
Abstract
In image classification, traditional kernels or feature mapping functions of Support Vector Machine(SVM) use discriminative features without considering the true nature of the data. Our work in this paper is motivated by the need to consider intrinsic distribution of L1 normalized histograms and develop a flexible feature mapping technique by combining histogram based features and distribution based density features. The proposed mapping technique contains prior knowledge about the the data which provides a flexible representation and thus increases the discriminative power of the classifier. Such flexibility is achieved due to the explanatory capabilities of Dirichlet, generalized Dirichlet and Beta-Liouville distributions to model proportional data. In addition to that, we present a general framework to estimate the parameters of these distributions by taking maximum likelihood (MLE) approach. Experimental results show that the proposed technique increases the effectiveness of SVM kernels for different computer vision tasks such as natural scene recognition, satellite image classification and human action recognition in videos.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".