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» Algorithmic Complexity Bounds on Future Prediction Errors
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SODA
2001
ACM
79views Algorithms» more  SODA 2001»
15 years 1 months ago
Learning Markov networks: maximum bounded tree-width graphs
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
David R. Karger, Nathan Srebro
FTCS
1997
57views more  FTCS 1997»
15 years 1 months ago
Predicting Physical Processes in the Presence of Faulty Sensor Readings
A common problem in the operation of mission critical control systems is that of determining the future value of a physical quantity based upon past measurements of it or of relat...
Matthew Clegg, Keith Marzullo
ECRTS
2004
IEEE
15 years 3 months ago
An Event Stream Driven Approximation for the Analysis of Real-Time Systems
This paper presents a new approach to understand the event stream model. Additionally a new approximation algorithm for the feasibility test of the sporadic and the generalized mu...
Karsten Albers, Frank Slomka
VTC
2007
IEEE
109views Communications» more  VTC 2007»
15 years 6 months ago
A Reliability-Aware LDPC Code Decoding Algorithm
— With the continuing downscaling of microelectronic technology, chip reliability becomes a great threat to the design of future complex microelectronic systems. Hence increasing...
Matthias Alles, Torben Brack, Norbert Wehn
JMLR
2010
121views more  JMLR 2010»
14 years 6 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor