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ICASSP
2011
IEEE
13 years 18 days ago
Improved model-based spectral compressive sensing via nested least squares
This paper introduces a new algorithm for reconstructing signals with sparse spectrums from noisy compressive measurements. The proposed model-based algorithm takes the signal str...
Mahdi Shaghaghi, Sergiy A. Vorobyov
JMLR
2006
145views more  JMLR 2006»
13 years 8 months ago
Ensemble Pruning Via Semi-definite Programming
An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead t...
Yi Zhang 0006, Samuel Burer, W. Nick Street
TSP
2008
102views more  TSP 2008»
13 years 8 months ago
Spatially Adaptive Estimation via Fitted Local Likelihood Techniques
Abstract--This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. T...
Vladimir Katkovnik, Vladimir Spokoiny
ICDM
2007
IEEE
148views Data Mining» more  ICDM 2007»
14 years 22 days ago
Sample Selection for Maximal Diversity
The problem of selecting a sample subset sufficient to preserve diversity arises in many applications. One example is in the design of recombinant inbred lines (RIL) for genetic a...
Feng Pan, Adam Roberts, Leonard McMillan, David Th...
ISNN
2005
Springer
14 years 2 months ago
Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error
In least squares support vector (LS-SVM), the key challenge lies in the selection of free parameters such as kernel parameters and tradeoff parameter. However, when a large number ...
Liefeng Bo, Ling Wang, Licheng Jiao