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» Face Class Modeling Using Mixture of SVMs
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CVPR
2012
IEEE
13 years 3 months ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
INTERSPEECH
2010
14 years 8 months ago
Canonical state models for automatic speech recognition
Current speech recognition systems are often based on HMMs with state-clustered Gaussian Mixture Models (GMMs) to represent the context dependent output distributions. Though high...
Mark J. F. Gales, Kai Yu
SIGIR
2006
ACM
15 years 7 months ago
Probabilistic latent query analysis for combining multiple retrieval sources
Combining the output from multiple retrieval sources over the same document collection is of great importance to a number of retrieval tasks such as multimedia retrieval, web retr...
Rong Yan, Alexander G. Hauptmann
AVSS
2007
IEEE
15 years 7 months ago
Vehicular traffic density estimation via statistical methods with automated state learning
This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a R...
Evan Tan, Jing Chen
AUSAI
2009
Springer
15 years 8 months ago
Enhancing MML Clustering Using Context Data with Climate Applications
Abstract. In Minimum Message Length (MML) clustering (unsupervised classification, mixture modelling) the aim is to infer a set of classes that best explains the observed data ite...
Gerhard Visser, David L. Dowe, Petteri Uotila