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ICASSP
2011
IEEE
12 years 11 months ago
Empirical divergence maximization for quantizer design: An analysis of approximation error
Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions tha...
Michael A. Lexa
CORR
2012
Springer
214views Education» more  CORR 2012»
12 years 3 months ago
Stochastic Low-Rank Kernel Learning for Regression
We present a novel approach to learn a kernelbased regression function. It is based on the use of conical combinations of data-based parameterized kernels and on a new stochastic ...
Pierre Machart, Thomas Peel, Liva Ralaivola, Sandr...
ATAL
2003
Springer
14 years 24 days ago
Towards a pareto-optimal solution in general-sum games
Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such ...
Sandip Sen, Stéphane Airiau, Rajatish Mukhe...
TSP
2008
116views more  TSP 2008»
13 years 7 months ago
Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory - Part I: Nash Equilibria
In this two-part paper, we propose a decentralized strategy, based on a game-theoretic formulation, to find out the optimal precoding/multiplexing matrices for a multipoint-to-mult...
Gesualdo Scutari, Daniel Pérez Palomar, Ser...
NIPS
2003
13 years 9 months ago
Feature Selection in Clustering Problems
A novel approach to combining clustering and feature selection is presented. It implements a wrapper strategy for feature selection, in the sense that the features are directly se...
Volker Roth, Tilman Lange