How to estimate probability of misclassification for n HMMs?

09 September 2019 1 8K Report

Hi,

I have n HMM models and I want to classify a process based on a sequence of observations.

Is there a technique to have an estimation (or an upper bound) of the probability of misclassification?

The classification error probability of your model is inversely proportionally given by its likelihood, which is internally used by the Baum-Welch algorithm in order to improve your model at each iteration.

Just be aware that any decent BW implementation uses -log-likelihood in order to avoid underflow.

Now, if you want to go a step further, read this paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5983107

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