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» Learning the Structure of Linear Latent Variable Models
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JMLR
2008
150views more  JMLR 2008»
13 years 8 months ago
Discriminative Learning of Max-Sum Classifiers
The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
Vojtech Franc, Bogdan Savchynskyy
BMCBI
2007
138views more  BMCBI 2007»
13 years 8 months ago
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic
Background: A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk asse...
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soin...
PERCOM
2007
ACM
14 years 8 months ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday
ICML
2009
IEEE
14 years 9 months ago
Learning dictionaries of stable autoregressive models for audio scene analysis
In this paper, we explore an application of basis pursuit to audio scene analysis. The goal of our work is to detect when certain sounds are present in a mixed audio signal. We fo...
Youngmin Cho, Lawrence K. Saul
ICASSP
2011
IEEE
13 years 17 days ago
Fourier expansion of hammerstein models for nonlinear acoustic system identification
We consider the task of acoustic system identification, where the input signal undergoes a memoryless nonlinear transformation before convolving with an unknown linear system. We...
Sarmad Malik, Gerald Enzner