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» Algorithmic Complexity Bounds on Future Prediction Errors
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STACS
1999
Springer
15 years 3 months ago
A Complete and Tight Average-Case Analysis of Learning Monomials
Abstract. We advocate to analyze the average complexity of learning problems. An appropriate framework for this purpose is introduced. Based on it we consider the problem of learni...
Rüdiger Reischuk, Thomas Zeugmann
COCO
2009
Springer
119views Algorithms» more  COCO 2009»
15 years 6 months ago
An Approximation Algorithm for Approximation Rank
One of the strongest techniques available for showing lower bounds on quantum communication complexity is the logarithm of the approximation rank of the communication matrix— th...
Troy Lee, Adi Shraibman
ICCV
2003
IEEE
16 years 1 months ago
Controlling Model Complexity in Flow Estimation
This paper describes a novel application of Statistical Learning Theory (SLT) to control model complexity in flow estimation. SLT provides analytical generalization bounds suitabl...
Zoran Duric, Fayin Li, Harry Wechsler, Vladimir Ch...
WONS
2005
IEEE
15 years 5 months ago
A Partition Prediction Algorithm for Service Replication in Mobile Ad Hoc Networks
Due to the mobility of nodes in Ad hoc networks, network topology is dynamic and unpredictable, which leads to frequent network partitioning. This partitioning disconnects many no...
Abdelouahid Derhab, Nadjib Badache, Abdelmadjid Bo...
ICASSP
2011
IEEE
14 years 3 months ago
Noise power gain as a measure of errors in discrete-time transversal estimators
The noise power gain (NPG) is addressed and the error bound (EB) is specified via NPG for a general p-shift linear timevariant finite impulse response (FIR) transversal estimato...
Yuriy S. Shmaliy, Oscar Gerardo Ibarra-Manzano