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» Sparseness Achievement in Hidden Markov Models
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FLAIRS
2004
13 years 9 months ago
Semi-Supervised Sequence Classification with HMMs
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervi...
Shi Zhong
ICMCS
2009
IEEE
146views Multimedia» more  ICMCS 2009»
13 years 5 months ago
Boosting multi-modal camera selection with semantic features
In this work semantic features are used to improve the results of the camera selection. These semantic features are group action, person action and person speaking. For this purpo...
Benedikt Hörnler, Dejan Arsic, Björn Sch...
IUI
2004
ACM
14 years 1 months ago
Sheepdog: learning procedures for technical support
Technical support procedures are typically very complex. Users often have trouble following printed instructions describing how to perform these procedures, and these instructions...
Tessa A. Lau, Lawrence D. Bergman, Vittorio Castel...
ICDAR
2007
IEEE
14 years 1 months ago
Minimum Error Discriminative Training for Radical-Based Online Chinese Handwriting Recognition
Free style Chinese handwriting recognition continues to pose a challenge to researchers due to the variety of Chinese writing styles. To recognize handwritten characters in an onl...
Y. Zhang, P. Liu, F. Soong
INTERSPEECH
2010
13 years 2 months ago
HMM-based text-to-articulatory-movement prediction and analysis of critical articulators
In this paper we present a method to predict the movement of a speaker's mouth from text input using hidden Markov models (HMM). We have used a corpus of human articulatory m...
Zhen-Hua Ling, Korin Richmond, Junichi Yamagishi