Maximum <i>A Posteriori</i> Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection
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Bibliographic record
Abstract
One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.
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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 | 0.000 | 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 it