Fundamentals of speech recognition
Fundamentals of speech recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Postprocessing of Recognized Strings Using Nonstationary Markovian Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Handwritten Numeral Character Classification Using Tolerant Rough Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Structural hidden Markov models for biometrics: Fusion of face and fingerprint
Pattern Recognition
A statistical multiresolution approach for face recognition using structural hidden Markov models
EURASIP Journal on Advances in Signal Processing
Structured Hidden Markov Model: A General Framework for Modeling Complex Sequences
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Learning Process Behavior with EDY: an Experimental Analysis
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
Conformation-based hidden Markov models: application to human face identification
IEEE Transactions on Neural Networks
Private predictions on hidden Markov models
Artificial Intelligence Review
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Standard hidden Markov models (HMM's) have been studied extensively in the last two decades. It is well known that these models assume state conditional independence of the observations. Therefore, they are inadequate for classification of complex and highly structured patterns. Nowadays, the need for new statistical models that are capable to cope with structural time series data is increasing. We propose in this paper a novel paradigm that we named "structural hidden Markov model" (SHMM). It extends traditional HMM's by partitioning the set of observation sequences into classes of equivalences. These observation sequences are related in the sense they all contribute to produce a particular local structure. We describe four basic problems that are assigned to a structural hidden Markov model: (1) probability evaluation, (2) statistical decoding, (3) local structure decoding, and (4) parameter estimation. We have applied SHMM in order to mine customers' preferences for automotive designs. The results reported in this application show that SHMM's outperform the traditional hidden Markov model with a 9% of increase in accuracy.