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DM
2010
77views more  DM 2010»
14 years 11 months ago
Representing (0, 1)-matrices by boolean circuits
A boolean circuit represents an n by n (0,1)-matrix A if it correctly computes the linear transformation y = Ax over GF(2) on all n unit vectors. If we only allow linear boolean f...
Stasys Jukna
ICASSP
2011
IEEE
14 years 3 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
CVPR
2009
IEEE
1528views Computer Vision» more  CVPR 2009»
16 years 3 months ago
Structured Output-Associative Regression
Structured outputs such as multidimensional vectors or graphs are frequently encountered in real world pattern recognition applications such as computer vision, natural language pr...
Liefeng Bo and Cristian Sminchisescu
CORR
2006
Springer
130views Education» more  CORR 2006»
14 years 11 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
ICIP
2008
IEEE
15 years 6 months ago
Hardware-friendly descreening
Conventional electrophotographic printers tend to produce Moir´e artifacts when used for printing images scanned from printed material such as books and magazines. We propose a n...
Hasib Siddiqui, Mireille Boutin, Charles A. Bouman