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» Approximation algorithms for budgeted learning problems
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SIAMCO
2000
117views more  SIAMCO 2000»
13 years 8 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn
COMBINATORICA
2010
13 years 3 months ago
Approximation algorithms via contraction decomposition
We prove that the edges of every graph of bounded (Euler) genus can be partitioned into any prescribed number k of pieces such that contracting any piece results in a graph of bou...
Erik D. Demaine, MohammadTaghi Hajiaghayi, Bojan M...
ICML
2008
IEEE
14 years 9 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
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
SODA
2008
ACM
184views Algorithms» more  SODA 2008»
13 years 10 months ago
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
The problem of maximizing a concave function f(x) in a simplex S can be solved approximately by a simple greedy algorithm. For given k, the algorithm can find a point x(k) on a k-...
Kenneth L. Clarkson