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» MuFeSaC: Learning When to Use Which Feature Detector
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CVPR
2006
IEEE
14 years 10 months ago
Training Deformable Models for Localization
We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the l...
Deva Ramanan, Cristian Sminchisescu
JMLR
2006
156views more  JMLR 2006»
13 years 7 months ago
Large Scale Multiple Kernel Learning
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
Sören Sonnenburg, Gunnar Rätsch, Christi...
GECCO
2005
Springer
183views Optimization» more  GECCO 2005»
14 years 1 months ago
802.11 network intrusion detection using genetic programming
Genetic Programming (GP) based Intrusion Detection Systems (IDS) use connection state network data during their training phase. These connection states are recorded as a set of fe...
Patrick LaRoche, A. Nur Zincir-Heywood
BMCBI
2008
116views more  BMCBI 2008»
13 years 8 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...
AAAI
2008
13 years 10 months ago
An Effective and Robust Method for Short Text Classification
Classification of texts potentially containing a complex and specific terminology requires the use of learning methods that do not rely on extensive feature engineering. In this w...
Victoria Bobicev, Marina Sokolova