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EMO
2001
Springer
71views Optimization» more  EMO 2001»
15 years 10 months ago
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
Abstract The performance of stochastic optimisers can be assessed experimentally on given problems by performing multiple optimisation runs, and analysing the results. Since an opt...
Viviane Grunert da Fonseca, Carlos M. Fonseca, And...
ICAI
2007
15 years 7 months ago
Dynamic Programming Algorithm for Training Functional Networks
Abstract— The paper proposes a dynamic programming algorithm for training of functional networks. The algorithm considers each node as a state. The problem is formulated as find...
Emad A. El-Sebakhy, Salahadin Mohammed, Moustafa E...
182
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ICDM
2009
IEEE
175views Data Mining» more  ICDM 2009»
15 years 3 months ago
Maximum Margin Clustering with Multivariate Loss Function
This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clust...
Bin Zhao, James Tin-Yau Kwok, Changshui Zhang
CVPR
2005
IEEE
16 years 8 months ago
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Stefan Roth, Michael J. Black
CVPR
2008
IEEE
16 years 8 months ago
Taylor expansion based classifier adaptation: Application to person detection
Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In th...
Cha Zhang, Raffay Hamid, Zhengyou Zhang