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» Frequency domain maximum likelihood estimation of linear dyn...
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JMLR
2008
209views more  JMLR 2008»
13 years 7 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
TASLP
2011
13 years 2 months ago
Advances in Missing Feature Techniques for Robust Large-Vocabulary Continuous Speech Recognition
— Missing feature theory (MFT) has demonstrated great potential for improving the noise robustness in speech recognition. MFT was mostly applied in the log-spectral domain since ...
Maarten Van Segbroeck, Hugo Van Hamme
ISBI
2004
IEEE
14 years 8 months ago
Covariance of Kinetic Parameter Estimators Based on Time Activity Curve Reconstructions: Preliminary Study on 1D Dynamic Imaging
We provide approximate expressions for the covariance matrix of kinetic parameter estimators based on time activity curve (TAC) reconstructions when TACs are modeled as a linear c...
Sangtae Ahn, Jeffrey A. Fessler, Thomas E. Nichols...
TWC
2008
201views more  TWC 2008»
13 years 7 months ago
A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments
Abstract--Positioning in indoor wireless environments is growing rapidly in importance and gains commercial interests in context-awareness applications. The essential challenge in ...
Shih-Hau Fang, Tsung-Nan Lin, Kun-Chou Lee
NIPS
1998
13 years 8 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis