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» Learning Polynomials with Queries: The Highly Noisy Case
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AI
2010
Springer
13 years 7 months ago
Understanding the scalability of Bayesian network inference using clique tree growth curves
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
Ole J. Mengshoel
BMCBI
2006
146views more  BMCBI 2006»
13 years 7 months ago
Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE
Background: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease...
Satoshi Niijima, Satoru Kuhara
ICALP
2009
Springer
14 years 7 months ago
Testing Fourier Dimensionality and Sparsity
We present a range of new results for testing properties of Boolean functions that are defined in terms of the Fourier spectrum. Broadly speaking, our results show that the propert...
Parikshit Gopalan, Ryan O'Donnell, Rocco A. Served...
TIP
2010
255views more  TIP 2010»
13 years 2 months ago
Image Super-Resolution Via Sparse Representation
This paper presents a new approach to single-image superresolution, based on sparse signal representation. Research on image statistics suggests that image patches can be wellrepre...
Jianchao Yang, John Wright, Thomas S. Huang, Yi Ma
PAMI
2008
216views more  PAMI 2008»
13 years 7 months ago
Annotating Images by Mining Image Search Results
Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel atte...
Xin-Jing Wang, Lei Zhang 0001, Xirong Li, Wei-Ying...