Layered hidden Markov model: Difference between revisions

Revision as of 05:12, 24 July 2012

Revision as of 09:01, 7 December 2012

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Instead of simply using the winning HMM at level <math>L+1</math> as an input symbol for the HMM at level <math>L</math> it is possible to use it as a [[probability generator]] by passing the complete [[probability distribution]] up the layers of the LHMM. Thus instead of having a "winner takes all" strategy where the most probable HMM is selected as an observation symbol, the likelihood <math>L(i)</math> of observing the <math>i</math>th HMM can be used in the recursion formula of the level <math>L</math> HMM to account for the uncertainty in the classification of the HMMs at level <math>L+1</math>. Thus, if the classification of the HMMs at level <math>n+1</math> is uncertain, it is possible to pay more attention to the a-priori information encoded in the HMM at level <math>L</math>.

A LHMM could in practice be transformed into a single layered HMM where all the different models are concatenated together. Some of the advantages that may be expected from using the LHMM over a large single layer HMM is that the LHMM is less likely to suffer from over-fitting[[overfitting]] since the individual sub-components are trained independently on smaller amounts of data. A consequence of this is that a significantly smaller amount of training data is required for the LHMM to achieve a performance comparable of the HMM. Another advantage is that the layers at the bottom of the LHMM, which are more sensitive to changes in the environment such as the type of sensors, sampling rate etc. can be retrained separately without altering the higher layers of the LHMM.

==See also==