Smooth On-line Learning Algorithms for Hidden Markov Models

@article{citeulike:945319, abstract = {he modeling and analysis of DNA and protein sequences in biology (Baldi et al. (1992) and (1993), Cardon and Stormo (1992), Haussler et al. (1992), Krogh et al. (1993), and references therein) and optical character recognition (Levin and Pieraccini (1993)). A first order HMMM is characterized by a set of states, an alphabet of symbols, a probability transition matrix T = (t ij ) and a probability emission matrix E = (e ij ). The parameter t ij (resp. e ij ) represents the probability of...}, author = {Baldi, Pierre and Chauvin, Yves }, citeulike-article-id = {945319}, journal = {Neural Computation}, keywords = {hidden\_markov\_models, incremental\_learning}, number = {2}, pages = {307--318}, posted-at = {2006-11-15 21:37:17}, priority = {2}, title = {Smooth On-line Learning Algorithms for Hidden Markov Models}, url = {http://citeseer.ist.psu.edu/baldi94smooth.html}, volume = {6}, year = {1994} }

TY - JOUR ID - citeulike:945319 L3 - citeulike-article-id:945319 TI - Smooth On-line Learning Algorithms for Hidden Markov Models JF - Neural Computation VL - 6 IS - 2 SP - 307 EP - 318 N2 - he modeling and analysis of DNA and protein sequences in biology (Baldi et al. (1992) and (1993), Cardon and Stormo (1992), Haussler et al. (1992), Krogh et al. (1993), and references therein) and optical character recognition (Levin and Pieraccini (1993)). A first order HMMM is characterized by a set of states, an alphabet of symbols, a probability transition matrix T = (t ij ) and a probability emission matrix E = (e ij ). The parameter t ij (resp. e ij ) represents the probability of... KW - hidden_markov_models KW - incremental_learning AU - Baldi, Pierre AU - Chauvin, Yves PY - 1994/// UR - http://citeseer.ist.psu.edu/baldi94smooth.html ER -