Sciweavers

FOCM
2006
65views more  FOCM 2006»
14 years 3 days ago
Approximation Methods for Supervised Learning
Let be an unknown Borel measure defined on the space Z := X
Ronald A. DeVore, Gerard Kerkyacharian, Dominique ...
CORR
2006
Springer
146views Education» more  CORR 2006»
14 years 4 days ago
Approximation Algorithms for Multi-Criteria Traveling Salesman Problems
In multi-criteria optimization, several objective functions are to be optimized. Since the different objective functions are usually in conflict with each other, one cannot conside...
Bodo Manthey, L. Shankar Ram
COMPUTING
2006
112views more  COMPUTING 2006»
14 years 4 days ago
Fast Summation of Radial Functions on the Sphere
Radial functions are a powerful tool in many areas of multidimensional approximation, especially when dealing with scattered data. We present a fast approximate algorithm for the ...
Jens Keiner, Stefan Kunis, Daniel Potts
ADCM
2008
187views more  ADCM 2008»
14 years 7 days ago
Approximation on the sphere using radial basis functions plus polynomials
In this paper we analyse a hybrid approximation of functions on the sphere S2 R3 by radial basis functions combined with polynomials, with the radial basis functions assumed to be...
Ian H. Sloan, Alvise Sommariva
UAI
1996
14 years 1 months ago
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomp...
David Maxwell Chickering, David Heckerman
UAI
2000
14 years 1 months ago
Value-Directed Belief State Approximation for POMDPs
We consider the problem belief-state monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might ap...
Pascal Poupart, Craig Boutilier
NC
1998
101views Neural Networks» more  NC 1998»
14 years 1 months ago
Evolutionary Optimized Tensor Product Bernstein Polynomials versus Backpropagation Networks
In this paper a new approach for approximation problems involving only few input and output parameters is presented and compared to traditional Backpropagation Neural Networks (BP...
Günther R. Raidl, Gabriele Kodydek
AAAI
1997
14 years 1 months ago
Bayes Networks for Estimating the Number of Solutions to a CSP
The problem of counting the number of solutions to a constraint satisfaction problem (CSP) is rephrased in terms of probability updating in Bayes networks. Approximating the proba...
Amnon Meisels, Solomon Eyal Shimony, Gadi Solotore...
NIPS
2003
14 years 1 months ago
Perspectives on Sparse Bayesian Learning
Recently, relevance vector machines (RVM) have been fashioned from a sparse Bayesian learning (SBL) framework to perform supervised learning using a weight prior that encourages s...
David P. Wipf, Jason A. Palmer, Bhaskar D. Rao
FLAIRS
2003
14 years 1 months ago
Indeterminacy and Rough Approximation
This paper deals with the problem of merging descriptions of approximate spatial location specified at different levels of granularity. We distinguish between the roughness of an...
Thomas Bittner