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» Experimental Evaluation of Hierarchical Hidden Markov Models
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CVPR
2007
IEEE
16 years 4 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 7 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 7 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 3 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 2 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 ...