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IPSN
2004
Springer
13 years 11 months ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
JMLR
2012
11 years 8 months ago
Multi Kernel Learning with Online-Batch Optimization
In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
Francesco Orabona, Jie Luo, Barbara Caputo
CDC
2010
IEEE
139views Control Systems» more  CDC 2010»
13 years 1 months ago
Q-learning and enhanced policy iteration in discounted dynamic programming
We consider the classical finite-state discounted Markovian decision problem, and we introduce a new policy iteration-like algorithm for finding the optimal state costs or Q-facto...
Dimitri P. Bertsekas, Huizhen Yu
ESANN
2006
13 years 7 months ago
Magnification control for batch neural gas
Neural gas (NG) constitutes a very robust clustering algorithm which can be derived as stochastic gradient descent from a cost function closely connected to the quantization error...
Barbara Hammer, Alexander Hasenfuss, Thomas Villma...
CDC
2009
IEEE
111views Control Systems» more  CDC 2009»
13 years 11 months ago
On fusion of information from multiple sensors in the presence of analog erasure links
— Consider multiple sensors that transmit data over analog erasure links to an estimation center. The sensors have access to distinct entries of the output vector of a linear and...
Vijay Gupta, Nuno C. Martins