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CORR
2010
Springer
141views Education» more  CORR 2010»
15 years 29 days ago
Learning Functions of Few Arbitrary Linear Parameters in High Dimensions
Let us assume that f is a continuous function defined on the unit ball of Rd , of the form f(x) = g(Ax), where A is a k×d matrix and g is a function of k variables for k ≪ d. ...
Massimo Fornasier, Karin Schnass, Jan Vybír...
107
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STOC
2009
ACM
119views Algorithms» more  STOC 2009»
16 years 3 months ago
Explicit construction of a small epsilon-net for linear threshold functions
We give explicit constructions of epsilon nets for linear threshold functions on the binary cube and on the unit sphere. The size of the constructed nets is polynomial in the dime...
Yuval Rabani, Amir Shpilka
JC
2011
87views more  JC 2011»
14 years 9 months ago
Lower bounds for the complexity of linear functionals in the randomized setting
Abstract. Hinrichs [3] recently studied multivariate integration defined over reproducing kernel Hilbert spaces in the randomized setting and for the normalized error criterion. I...
Erich Novak, Henryk Wozniakowski
ICML
2007
IEEE
16 years 3 months ago
On learning linear ranking functions for beam search
Beam search is used to maintain tractability in large search spaces at the expense of completeness and optimality. We study supervised learning of linear ranking functions for con...
Yuehua Xu, Alan Fern
ICML
2004
IEEE
16 years 3 months ago
Convergence of synchronous reinforcement learning with linear function approximation
Synchronous reinforcement learning (RL) algorithms with linear function approximation are representable as inhomogeneous matrix iterations of a special form (Schoknecht & Merk...
Artur Merke, Ralf Schoknecht