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» Learning of Boolean Functions Using Support Vector Machines
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GECCO
2010
Springer
184views Optimization» more  GECCO 2010»
14 years 14 days ago
A mono surrogate for multiobjective optimization
Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective O...
Ilya Loshchilov, Marc Schoenauer, Michèle S...
BMCBI
2008
116views more  BMCBI 2008»
13 years 9 months ago
The combination approach of SVM and ECOC for powerful identification and classification of transcription factor
Background: Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network co...
Guangyong Zheng, Ziliang Qian, Qing Yang, Chaochun...
ICDAR
2009
IEEE
14 years 3 months ago
Online Recognition of Multi-Stroke Symbols with Orthogonal Series
We propose an efficient method to recognize multi-stroke handwritten symbols. The method is based on computing the truncated Legendre-Sobolev expansions of the coordinate functio...
Oleg Golubitsky, Stephen M. Watt
ICIP
2001
IEEE
14 years 10 months ago
Face detection using large margin classifiers
Large margin classifiers have demonstrated their advantages in many visual learning tasks, and have attracted much attention in vision and image processing communities. In this pa...
Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
ICML
2006
IEEE
14 years 10 months ago
Concept boundary detection for speeding up SVMs
Support Vector Machines (SVMs) suffer from an O(n2 ) training cost, where n denotes the number of training instances. In this paper, we propose an algorithm to select boundary ins...
Navneet Panda, Edward Y. Chang, Gang Wu