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» On the convergence of Hill's method
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KDD
2008
ACM
120views Data Mining» more  KDD 2008»
16 years 1 months ago
Multi-class cost-sensitive boosting with p-norm loss functions
We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establ...
Aurelie C. Lozano, Naoki Abe
ICASSP
2010
IEEE
15 years 1 months ago
Iterated smoothing for accelerated gradient convex minimization in signal processing
In this paper, we consider the problem of minimizing a non-smooth convex problem using first-order methods. The number of iterations required to guarantee a certain accuracy for ...
Tobias Lindstrøm Jensen, Jan Østerga...
IJON
2007
99views more  IJON 2007»
15 years 1 months ago
A relative trust-region algorithm for independent component analysis
In this paper we present a method of parameter optimization, relative trust-region learning, where the trust-region method and the relative optimization [21] are jointly exploited...
Heeyoul Choi, Seungjin Choi
JSCIC
2007
89views more  JSCIC 2007»
15 years 1 months ago
Adjoint Recovery of Superconvergent Linear Functionals from Galerkin Approximations. The One-dimensional Case
In this paper, we extend the adjoint error correction of Pierce and Giles [SIAM Review, 42 (2000), pp. 247-264] for obtaining superconvergent approximations of functionals to Gale...
Bernardo Cockburn, Ryuhei Ichikawa
EOR
2007
117views more  EOR 2007»
15 years 1 months ago
Simultaneous perturbation stochastic approximation of nonsmooth functions
A simultaneous perturbation stochastic approximation (SPSA) method has been developed in this paper, using the operators of perturbation with the Lipschitz density function. This ...
Vaida Bartkute, Leonidas Sakalauskas