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JMLR
2002
106views more  JMLR 2002»
13 years 9 months ago
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...
COCOON
2009
Springer
14 years 1 months ago
Optimal Transitions for Targeted Protein Quantification: Best Conditioned Submatrix Selection
Multiple reaction monitoring (MRM) is a mass spectrometric method to quantify a specified set of proteins. In this paper, we identify a problem at the core of MRM peptide quantific...
Rastislav Srámek, Bernd Fischer, Elias Vica...
ICS
2000
Tsinghua U.
14 years 1 months ago
Fast greedy weighted fusion
Loop fusion is important to optimizing compilers because it is an important tool in managing the memory hierarchy. By fusing loops that use the same data elements, we can reduce t...
Ken Kennedy
IPL
2006
80views more  IPL 2006»
13 years 9 months ago
BubbleSearch: A simple heuristic for improving priority-based greedy algorithms
We introduce BubbleSearch, a general approach for extending priority-based greedy heuristics. Following the framework recently developed by Borodin et al., we consider priority al...
Neal Lesh, Michael Mitzenmacher
TSP
2008
124views more  TSP 2008»
13 years 9 months ago
Dictionary Preconditioning for Greedy Algorithms
This article introduces the concept of sensing dictionaries. It presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching Pursuit which improves their...
Karin Schnass, Pierre Vandergheynst