Distribution Based Feature Mapping for Classifying Count Data
Bibliographic record
Abstract
In this paper, we propose a statistically flexible feature mapping technique for count data which are very common in data analysis and pattern recognition applications. In particular, we are interested in supervised learning to improve the existing non-linear classification techniques using Support Vector Machine (SVM). Perfect representation of the data through a kernel function is strongly dependent on the structure of the data. Thus, choosing an appropriate kernel function or feature mapping technique is necessary to get higher accuracy and in this paper, we address this problem by proposing a new feature mapping function derived from Dirichlet Multinomial and Generalized Dirichlet Multinomial distributions for count data. In order to determine the parameters of the distributions, two approaches namely Expectation Maximization (EM) and Minorization-Maximization (MM) are employed. We evaluate our model by experimenting on two different classification tasks concerning natural scene recognition from images and human action recognition from videos. The results show that, incorporating proposed feature mapping technique with different kernels comparatively gives better results than the base kernels in different classification tasks.
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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 | 0.001 |
| Open science | 0.001 | 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".