For example , the forward-backward algorithm ( Baum , 1972 ) trains only Hidden Markov Models , while ( Ristad and Yianilos , 1996 ) trains only stochastic edit distance .
learning string edit distance
E Ristad P Yianilos
Not only do these methods require additional programming outside the toolkit , but they are limited to particular kinds of models and training regimens . For example , the forward-backward algorithm ( Baum , 1972 ) trains only Hidden Markov Models , while ( Ristad and Yianilos , 1996 ) trains only stochastic edit distance . In short , current finite-state toolkits include no training algorithms , because none exist for the large space of statistical models that the toolkits can in principle describe and run .
For example , the forward-backward algorithm ( Baum , 1972 ) trains only Hidden Markov Models , while ( @@CITATION ) trains only stochastic edit distance .