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» Sparseness Achievement in Hidden Markov Models
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ICPR
2008
IEEE
14 years 2 months ago
A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory
In this paper, we propose an automatic system that recognizes both isolated and continuous gestures for Arabic numbers (0-9) in real-time based on Hidden Markov Model (HMM). To ha...
Mahmoud Elmezain, Ayoub Al-Hamadi, Jörg Appen...
NAACL
1994
13 years 8 months ago
Techniques to Achieve an Accurate Real-Time Large-Vocabulary Speech Recognition System
In addressing the problem of achieving high-accuracy real-time speech recognition systems, we focus on recognizing speech from ARPA's20,000-word Wall Street Journal (WSJ) tas...
Hy Murveit, Peter Monaco, Vassilios Digalakis, Joh...
ECAI
2004
Springer
14 years 28 days ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
PR
2010
147views more  PR 2010»
13 years 6 months ago
Minimum classification error learning for sequential data in the wavelet domain
Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted ...
D. Tomassi, Diego H. Milone, L. Forzani
JMLR
2010
202views more  JMLR 2010»
13 years 2 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...