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TNN
2008
181views more  TNN 2008»
13 years 8 months ago
Optimized Approximation Algorithm in Neural Networks Without Overfitting
In this paper, an optimized approximation algorithm (OAA) is proposed to address the overfitting problem in function approximation using neural networks (NNs). The optimized approx...
Yinyin Liu, Janusz A. Starzyk, Zhen Zhu
STOC
2004
ACM
109views Algorithms» more  STOC 2004»
14 years 8 months ago
Approximating the cut-norm via Grothendieck's inequality
The cut-norm ||A||C of a real matrix A = (aij)iR,jS is the maximum, over all I R, J S of the quantity | iI,jJ aij|. This concept plays a major role in the design of efficient app...
Noga Alon, Assaf Naor
CVPR
2010
IEEE
13 years 6 months ago
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
Anders Eriksson, Anton van den Hengel
STOC
2007
ACM
146views Algorithms» more  STOC 2007»
14 years 8 months ago
Playing games with approximation algorithms
In an online linear optimization problem, on each period t, an online algorithm chooses st S from a fixed (possibly infinite) set S of feasible decisions. Nature (who may be adve...
Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett
IPSN
2005
Springer
14 years 2 months ago
The sensor selection problem for bounded uncertainty sensing models
We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be in...
Volkan Isler, Ruzena Bajcsy