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» Missing Data Estimation Using Polynomial Kernels
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99
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TSP
2008
118views more  TSP 2008»
15 years 10 days ago
Approximating Functions From Sampled Fourier Data Using Spline Pseudofilters
Recently, new polynomial approximation formulas were proposed for the reconstruction of compactly supported piecewise smooth functions from Fourier data. Formulas for zero and firs...
Ana Gabriela Martínez, Alvaro R. De Pierro
NECO
1998
121views more  NECO 1998»
15 years 3 days ago
Nonlinear Time-Series Prediction with Missing and Noisy Data
We derive solutions for the problem of missing and noisy data in nonlinear timeseries prediction from a probabilistic point of view. We discuss different approximations to the so...
Volker Tresp, Reimar Hofmann
BMCBI
2008
190views more  BMCBI 2008»
15 years 17 days ago
Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many meth...
Guy N. Brock, John R. Shaffer, Richard E. Blakesle...
116
Voted
ICANN
2009
Springer
15 years 4 months ago
Classification Based on Combination of Kernel Density Estimators
Abstract. A new classification algorithm based on combination of kernel density estimators is introduced. The method combines the estimators with different bandwidths what can be i...
Mateusz Kobos, Jacek Mandziuk
111
Voted
CSL
2007
Springer
15 years 12 days ago
On noise masking for automatic missing data speech recognition: A survey and discussion
Automatic speech recognition (ASR) has reached very high levels of performance in controlled situations. However, the performance degrades significantly when environmental noise ...
Christophe Cerisara, Sébastien Demange, Jea...