Modified student's <i>t</i> ‐hidden Markov model for pattern recognition and classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications; however, recently the Student's t ‐mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t ‐mixture distribution as its hidden state is the Student's t ‐hidden Markov model (SHMM). The authors propose a novel Student's t ‐hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation–maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.
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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 | 0.001 | 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