Research map / Time series / Causal and generative time series
Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model transitions between AR processes with an HMM layer (ARHMM-MIL vs MARMIL mixture without transitions) (ablation) | ARHMM-MIL · Trainspotting and Opportunity datasets | bag and instance AUC | Helped |
| ARHMM-MIL vs miSVM, DPMIL, miGraph baselines on bag-level AUC | ARHMM-MIL · 8 activity datasets | bag AUC: best in 6/8 datasets | Helped |
| ARHMM-MIL vs baselines on instance-level AUC | ARHMM-MIL · 8 activity datasets | instance AUC: best in 7/8 datasets | Helped |
| statistical significance of ARHMM-MIL gains over other algorithms | ARHMM-MIL · 8 activity datasets | AUC | No clear effect |
| increase range of SVM C parameter | SVM-based baselines · tuning | AUC | No clear effect |
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