ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
This paper presents a new approach to estimate “universal” phoneme posterior probabilities for mixed language speech recognition. More specifically, we propose a new theoreti...
CORR
2010
Springer
13 years 1 months ago
2010
Springer
In this paper we show that, in case of uncertainties during the estimation, overconfident posterior probabilities tend to mislead the performance of soft-decoders. Maximum likeliho...
BMCBI
2005
13 years 4 months ago
2005
Background: A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, becaus...
IJAR
2008
13 years 4 months ago
2008
Cooperative multiagent probabilistic inference can be applied in areas such as building surveillance and complex system diagnosis to reason about the states of the distributed unc...
NIPS
2001
13 years 5 months ago
2001
In previous work on "transformed mixtures of Gaussians" and "transformed hidden Markov models", we showed how the EM algorithm in a discrete latent variable mo...
ESANN
2006
13 years 5 months ago
2006
Abstract. We present a new method for analyzing classifiers by visualization, which we call visual nonlinear discriminant analysis. Classifiers that output posterior probabilities ...
EMNLP
2004
13 years 6 months ago
2004
We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and max...
ACL
2008
13 years 6 months ago
2008
Word and n-gram posterior probabilities estimated on N-best hypotheses have been used to improve the performance of statistical machine translation (SMT) in a rescoring framework....
ICIP
1998
IEEE
13 years 8 months ago
1998
IEEE
A new approach for the segmentation of still and video SAR images is described in this paper. A priori knowledge about the objects present in the image, e.g., target, shadow, and ...
ICASSP
2008
IEEE
13 years 11 months ago
2008
IEEE
Several classification scenarios employ multiple independently trained classifiers and the outputs of these classifiers need to be combined. However, since each of the trained ...