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» Learning from Highly Structured Data by Decomposition
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DATE
2009
IEEE
120views Hardware» more  DATE 2009»
15 years 4 months ago
Optimizing data flow graphs to minimize hardware implementation
Abstract - This paper describes an efficient graphbased method to optimize data-flow expressions for best hardware implementation. The method is based on factorization, common su...
Daniel Gomez-Prado, Q. Ren, Maciej J. Ciesielski, ...
ICMCS
2008
IEEE
207views Multimedia» more  ICMCS 2008»
15 years 4 months ago
Structure learning in a Bayesian network-based video indexing framework
Several stochastic models provide an effective framework to identify the temporal structure of audiovisual data. Most of them need as input a first video structure, i.e. connecti...
Siwar Baghdadi, Guillaume Gravier, Claire-Hé...
IJCV
2010
186views more  IJCV 2010»
14 years 8 months ago
An Approach to the Parameterization of Structure for Fast Categorization
A decomposition is described, which parameterizes the geometry and appearance of contours and regions of gray-scale images with the goal of fast categorization. To express the con...
Christoph Rasche
79
Voted
WWW
2008
ACM
15 years 10 months ago
Learning to classify short and sparse text & web with hidden topics from large-scale data collections
This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from larges...
Xuan Hieu Phan, Minh Le Nguyen, Susumu Horiguchi
ISMB
1993
14 years 11 months ago
Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools
Weintroduce a parallel approach, "DT-SELECT," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-SELECTis able to ra...
Kevin J. Cherkauer, Jude W. Shavlik