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» Relevant subtask learning by constrained mixture models
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ICMCS
2009
IEEE
97views Multimedia» more  ICMCS 2009»
13 years 3 months ago
Some new directions in graph-based semi-supervised learning
In this position paper, we first review the state-of-the-art in graph-based semi-supervised learning, and point out three limitations that are particularly relevant to multimedia ...
Xiaojin Zhu, Andrew B. Goldberg, Tushar Khot
IJCAI
2007
13 years 7 months ago
A Theoretical Framework for Learning Bayesian Networks with Parameter Inequality Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
ICASSP
2011
IEEE
12 years 9 months ago
HNM-based MFCC+F0 extractor applied to statistical speech synthesis
Currently, the statistical framework based on Hidden Markov Models (HMMs) plays a relevant role in speech synthesis, while voice conversion systems based on Gaussian Mixture Model...
Daniel Erro, Iñaki Sainz, Eva Navas, Inma H...
ICASSP
2009
IEEE
14 years 15 days ago
Multi-view tracking of articulated human motion in silhouette and pose manifolds
This paper presents a multi-view articulated human motion tracking framework using particle filter with manifold learning through Gaussian process latent variable model. The dime...
Feng Guo, Gang Qian
ICASSP
2008
IEEE
14 years 7 days ago
French prominence: A probabilistic framework
Identification of prosodic phenomena is of first importance in prosodic analysis and modeling. In this paper, we introduce a new method for automatic prosodic phenomena labellin...
Nicolas Obin, Xavier Rodet, Anne Lacheret-Dujour