Parallel implementation of dynamic naive Bayesian classifier
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Data sets based on Toy Robot data set
| Data set type | Average success rate [%] |
|---|---|
| Discrete | 65 |
| Continuous | 42 |
| Bivariate | 76 |
| Gaussian mixture (without hint) | 96 |
| Gaussian mixture (with hint) | 99 |
The average success rate means the average percentage of hidden states inferred correctly.
There are two main reasons for relatively low overall sucess rate:
- Only about 90% of observed symbols are accurate
- There are multiple transitions to hidden states with the same observed symbol
| Property | Value |
|---|---|
| Number of hidden states | 10 |
| Sequence length | 200 |
| Observed discrete variables | 5 |
| Observed continuous variables | 5 |
| Learning set length (#sequences) | 1000 |
| Testing set length (#sequences) | 200 |
| Max Gaussians per mixture | 3 |
| Transitions per hidden state | 5 |
| Property | Value |
|---|---|
| Processor | 2× 8-core Intel Xeon E5-2650 v2 2.6 GHz |
| Memory | 15 GB |
| Disk | 10 GB HDD |
| Property | Workers=1 | Workers=2 | Workers=4 | Workers=8 | Workers=15 |
|---|---|---|---|---|---|
| Learning time speed up | 1 | 1.3 | 1.5 | 1.8 | 2 |