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» Structured Output Learning with High Order Loss Functions
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ISNN
2007
Springer
15 years 3 months ago
Neural-Based Separating Method for Nonlinear Mixtures
A neural-based method for source separation in nonlinear mixture is proposed in this paper. A cost function, which consists of the mutual information and partial moments of the out...
Ying Tan
94
Voted
ICCV
2011
IEEE
13 years 9 months ago
Learning a Category Independent Object Detection Cascade
Cascades are a popular framework to speed up object detection systems. Here we focus on the first layers of a category independent object detection cascade in which we sample a l...
Esa Rahtu, Juho Kannala, Matthew Blaschko
BMCBI
2007
116views more  BMCBI 2007»
14 years 9 months ago
Clustering protein environments for function prediction: finding PROSITE motifs in 3D
Background: Structural genomics initiatives are producing increasing numbers of threedimensional (3D) structures for which there is little functional information. Structure-based ...
Sungroh Yoon, Jessica C. Ebert, Eui-Young Chung, G...
BMCBI
2005
84views more  BMCBI 2005»
14 years 9 months ago
A method of precise mRNA/DNA homology-based gene structure prediction
Background: Accurate and automatic gene finding and structural prediction is a common problem in bioinformatics, and applications need to be capable of handling non-canonical spli...
Alexander G. Churbanov, Mark Pauley, Daniel Quest,...
MIAR
2010
IEEE
14 years 8 months ago
Manifold Learning for Image-Based Gating of Intravascular Ultrasound(IVUS) Pullback Sequences
Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel structures. The IVUS frames are acquired by pulling the ca...
Gozde Gul Isguder, Gözde B. Ünal, Martin...