A Finite Gamma Mixture Model-Based Discriminative Learning Frameworks
Bibliographic record
Abstract
It is well-known that classification tasks can be approached using either generative models or discriminative ones. While the goal of generative approaches is to learn class-conditional densities, the main goal of discriminative techniques is to learn decision boundaries directly without taking into account class-conditional densities. In classic supervised learning, we would usually represent a given object (an image, for instance) by a vector of D real-valued features and then select a given generative or discriminative approach to perform classification. In many applications, however, the object can be represented by a set (or) bag of vectors. Recent developments in machine learning, along with powerful computational tools, have enabled researchers to develop more sophisticated models to handle such applications using the so-called hybrid generative discriminative models. The main idea is based on exploiting the advantages of both families of models. Thus, the success of such an approach depends on the choice of an appropriate discriminative technique and a suitable generative one. The goal of this paper is to develop a hybrid generative discriminative framework based on support vector machine and Gamma mixture. In particular, we focus on the generation of kernels when examples (images, for instance) are structured data (i.e. described by sets of vectors) modeled by Gamma mixtures. Experimental results on real-world challenging applications, namely 3D shape class recognition, object categorization, and video event analysis, show the effectiveness of the proposed framework.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".