Fundamentals of speech recognition
Fundamentals of speech recognition
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Postprocessing of Recognized Strings Using Nonstationary Markovian Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Estimation of probabilistic context-free grammars
Computational Linguistics
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
Hi-index | 0.00 |
We propose in this paper a novel paradigm that we named "structural hidden Markov model" (SHMM). It extends traditional hidden Markov models (HMMs) by considering observations as strings derived by a probabilistic context-free grammar. These observations are related in the sense they all contribute to produce a particular structure. SHMMs overcome the limit of state conditional independence of the observations in HMMs. Thus they are capable to cope with structural time series data. We have applied SHMM to data mine customers' preferences for automotive designs. A 5-fold cross-validation has shown a 9% increase of SHMM accuracy over HMM.