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ICPR
2002
IEEE
16 years 4 months ago
A Fast Leading Eigenvector Approximation for Segmentation and Grouping
We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is base...
Antonio Robles-Kelly, Sudeep Sarkar, Edwin R. Hanc...
FOCS
2004
IEEE
15 years 7 months ago
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
We consider a stochastic variant of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary d...
Brian C. Dean, Michel X. Goemans, Jan Vondrá...
DLT
2003
15 years 5 months ago
NFA Reduction Algorithms by Means of Regular Inequalities
We present different techniques for reducing the number of states and transitions in nondeterministic automata. These techniques are based on the two preorders over the set of st...
Jean-Marc Champarnaud, Fabien Coulon
135
Voted
ALT
2004
Springer
16 years 23 days ago
Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm
We consider a two-layer network algorithm. The first layer consists of an uncountable number of linear units. Each linear unit is an LMS algorithm whose inputs are first “kerne...
Mark Herbster
ICC
2009
IEEE
128views Communications» more  ICC 2009»
15 years 10 months ago
Approximate Flow-Aware Networking
—A new variation of the Flow-Aware Networking (FAN) concept is presented in the paper. The proposed solution is based on the Approximate Fair Dropping algorithm and called by us ...
Jerzy Domzal, Andrzej Jajszczyk