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» A Theory of Redo Recovery
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ICAISC
2004
Springer
15 years 3 months ago
Application of Rough Sets and Neural Networks to Forecasting University Facility and Administrative Cost Recovery
This paper presents a novel approach to financial time series analysis and prediction. It is mainly devoted to the problem of forecasting university facility and administrative co...
Tomasz G. Smolinski, Darrel L. Chenoweth, Jacek M....
ICASSP
2011
IEEE
14 years 1 months ago
Compressive sensing meets game theory
We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in underdetermined linear regression problems. Given f = Φα∗ + µ, the ...
Sina Jafarpour, Robert E. Schapire, Volkan Cevher
NIPS
2008
14 years 11 months ago
Sparse Signal Recovery Using Markov Random Fields
Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the th...
Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Ric...
TSP
2010
14 years 4 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
WDAG
2010
Springer
230views Algorithms» more  WDAG 2010»
14 years 8 months ago
Implementing Fault-Tolerant Services Using State Machines: Beyond Replication
Abstract—This paper describes a method to implement faulttolerant services in distributed systems based on the idea of fused state machines. The theory of fused state machines us...
Vijay K. Garg