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145
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ICASSP
2010
IEEE
15 years 3 months ago
A minimax approach to Bayesian estimation with partial knowledge of the observation model
We address the problem of Bayesian estimation where the statistical relation between the signal and measurements is only partially known. We propose modeling partial Baysian knowl...
Tomer Michaeli, Yonina C. Eldar
140
Voted
CJ
2010
131views more  CJ 2010»
15 years 1 months ago
Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks
an be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The...
Duncan Gillies, David Thornley, Chatschik Bisdikia...
147
Voted
PAMI
2008
162views more  PAMI 2008»
15 years 3 months ago
Dimensionality Reduction of Clustered Data Sets
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Guido Sanguinetti
124
Voted
DATE
2004
IEEE
114views Hardware» more  DATE 2004»
15 years 7 months ago
Workload Characterization Model for Tasks with Variable Execution Demand
The analysis of real-time properties of an embedded system usually relies on the worst-case execution times (WCET) of the tasks to be executed. In contrast to that, in real world ...
Alexander Maxiaguine, Simon Künzli, Lothar Th...
141
Voted
ICML
2010
IEEE
15 years 4 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre