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SODA
2000
ACM
85views Algorithms» more  SODA 2000»
15 years 5 months ago
Improved bounds on the sample complexity of learning
We present a new general upper bound on the number of examples required to estimate all of the expectations of a set of random variables uniformly well. The quality of the estimat...
Yi Li, Philip M. Long, Aravind Srinivasan
NIPS
1996
15 years 5 months ago
Radial Basis Function Networks and Complexity Regularization in Function Learning
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
Adam Krzyzak, Tamás Linder
CORR
2010
Springer
103views Education» more  CORR 2010»
15 years 4 months ago
Asymptotic Learning Curve and Renormalizable Condition in Statistical Learning Theory
Bayes statistics and statistical physics have the common mathematical structure, where the log likelihood function corresponds to the random Hamiltonian. Recently, it was discovere...
Sumio Watanabe
JSC
2006
95views more  JSC 2006»
15 years 4 months ago
Polyhedral conditions for the nonexistence of the MLE for hierarchical log-linear models
We provide a polyhedral description of the conditions for the existence of the maximum likelihood estimate (MLE) for a hierarchical log-linear model. The MLE exists if and only if...
Nicholas Eriksson, Stephen E. Fienberg, Alessandro...
HEURISTICS
2002
65views more  HEURISTICS 2002»
15 years 4 months ago
Probability Distribution of Solution Time in GRASP: An Experimental Investigation
A GRASP (greedy randomized adaptive search procedure) is a multi-start metaheuristic for combinatorial optimization. We study the probability distributions of solution time to a su...
Renata M. Aiex, Mauricio G. C. Resende, Celso C. R...