In statistical pattern recognition, the principal task is to
classify abstract data sets. Instead of using robust but
computational expensive algorithms it is possible to combine
`weak´ classifiers that can be employed in solving complex
classification tasks.
In this comparative study,
we will examine the effectiveness of the commonly used hybrid
schemes - especially those used for speech recognition problems -
concentrating on cases which employ different combinations of
classifiers
