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» Experimental Evaluation of Hierarchical Hidden Markov Models
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CVPR
2007
IEEE
16 years 8 months ago
Real-time Gesture Recognition with Minimal Training Requirements and On-line Learning
In this paper, we introduce the semantic network model (SNM), a generalization of the hidden Markov model (HMM) that uses factorization of state transition probabilities to reduce...
Stjepan Rajko, Gang Qian, Todd Ingalls, Jodi James
ICAPR
2005
Springer
15 years 11 months ago
Hierarchical Clustering of Dynamical Systems Based on Eigenvalue Constraints
Abstract. This paper addresses the clustering problem of hidden dynamical systems behind observed multivariate sequences by assuming an interval-based temporal structure in the seq...
Hiroaki Kawashima, Takashi Matsuyama
ICMCS
2005
IEEE
173views Multimedia» more  ICMCS 2005»
15 years 11 months ago
A Multi-Modal Mixed-State Dynamic Bayesian Network for Robust Meeting Event Recognition from Disturbed Data
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones,...
Marc Al-Hames, Gerhard Rigoll
AAAI
2000
15 years 7 months ago
Information Extraction with HMM Structures Learned by Stochastic Optimization
Recent research has demonstrated the strong performance of hidden Markov models applied to information extraction--the task of populating database slots with corresponding phrases...
Dayne Freitag, Andrew McCallum
ICML
2005
IEEE
16 years 7 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...