276 search results - page 24 / 56 » PAC-Learning of Markov Models with Hidden State |
DMSN
2008
ACM
13 years 10 months ago
2008
ACM
When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two ...
ICGI
2010
Springer
13 years 7 months ago
2010
Springer
In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time length...
NIPS
2003
13 years 9 months ago
2003
In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an artificially reduced set of states; this is due in large part to the fac...
INTERSPEECH
2010
13 years 3 months ago
2010
We extend our earlier work on deep-structured conditional random field (DCRF) and develop deep-structured hidden conditional random field (DHCRF). We investigate the use of this n...
CSL
1999
Springer
13 years 8 months ago
1999
Springer
This paper presents a new approach to speech synthesis in which a set of cross-word decision-tree state-clustered context-dependent hidden Markov models are used to define a set o...