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» Learning nonlinear dynamic models
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88
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IJON
2006
99views more  IJON 2006»
15 years 25 days ago
Learning vector quantization: The dynamics of winner-takes-all algorithms
Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated accordin...
Michael Biehl, Anarta Ghosh, Barbara Hammer
108
Voted
ISBI
2008
IEEE
16 years 1 months ago
Segmentation of the evolving left ventricle by learning the dynamics
We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learn...
Walter Sun, Müjdat Çetin, Raymond Chan...
ICML
2004
IEEE
16 years 1 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
110
Voted
IFM
2009
Springer
124views Formal Methods» more  IFM 2009»
15 years 7 months ago
Dynamic Path Reduction for Software Model Checking
We present the new technique of dynamic path reduction (DPR), which allows one to prune redundant paths from the state space of a program under verification. DPR is a very general...
Zijiang Yang, Bashar Al-Rawi, Karem Sakallah, Xiao...
TMI
2008
138views more  TMI 2008»
15 years 8 days ago
Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...