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» On the Complexity of Exclusion Algorithms for Optimization
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ICPR
2010
IEEE
15 years 9 months ago
Fast Training of Object Detection Using Stochastic Gradient Descent
Training datasets for object detection problems are typically very large and Support Vector Machine (SVM) implementations are computationally complex. As opposed to these complex ...
Rob Wijnhoven, Peter H. N. De With
JMLR
2010
121views more  JMLR 2010»
15 years 1 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
MOBIHOC
2008
ACM
16 years 6 months ago
Self-monitoring for sensor networks
Local monitoring is an effective mechanism for the security of wireless sensor networks (WSNs). Existing schemes assume the existence of sufficient number of active nodes to carry...
Dezun Dong, Yunhao Liu, Xiangke Liao
GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 11 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...
ECAI
2008
Springer
15 years 8 months ago
Structure Learning of Markov Logic Networks through Iterated Local Search
Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
Marenglen Biba, Stefano Ferilli, Floriana Esposito