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» Approximation Methods for Supervised Learning
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FLAIRS
2004
13 years 9 months ago
Multimodal Function Optimization Using Local Ruggedness Information
In multimodal function optimization, niching techniques create diversification within the population, thus encouraging heterogeneous convergence. The key to the effective diversif...
Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles
NIPS
2003
13 years 8 months ago
On the Dynamics of Boosting
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in lo...
Cynthia Rudin, Ingrid Daubechies, Robert E. Schapi...
IJON
2006
131views more  IJON 2006»
13 years 7 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
TOG
2010
64views more  TOG 2010»
13 years 2 months ago
K-set tilable surfaces
This paper introduces a method for optimizing the tiles of a quadmesh. Given a quad-based surface, the goal is to generate a set of K quads whose instances can produce a tiled sur...
Chi-Wing Fu, Chi-Fu Lai, Ying He 0001, Daniel Cohe...
ICASSP
2011
IEEE
12 years 11 months ago
Robust nonparametric regression by controlling sparsity
Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeat...
Gonzalo Mateos, Georgios B. Giannakis