For classifying a sequence of instances, are there any specific circumstances that make Hidden Markov Models (HMMs) more accurate than Conditional Random Fields (CRFs)? I have seen several papers that show CRFs outperforming HMMs, but none showing the reverse (granted, my sample size is fairly small so far).
Any real world examples would be great, since I'm still working to understand the differences conceptually.
This is a late answer by 7 years, but. I applied HMM and linear CRF to a falling detection dataset my research group collected, a time step consist of 100+ IMU features. The HMM performs better as I was able to handcraft HMM transition matrix with respect to the human falling dynamics, that is state transitions(falling phases) are restricted to go from 1 to 2, 2 to 3, then stay infinitely in 3. I was only using a python hmm library and CRFsuite python binding blindly. Maybe the linear CRF can be tweaked like what I did to HMM.
