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INFORMATICALT
2011
112views more  INFORMATICALT 2011»
15 years 1 months ago
The Minimum Density Power Divergence Approach in Building Robust Regression Models
It is well known that in situations involving the study of large datasets where influential observations or outliers maybe present, regression models based on the Maximum Likeliho...
Alessandra Durio, Ennio Davide Isaia
INTERSPEECH
2010
15 years 1 months ago
Invariant integration features combined with speaker-adaptation methods
Speaker-normalization and -adaptation methods are essential components of state-of-the-art speech recognition systems nowadays. Recently, so-called invariant integration features ...
Florian Müller, Alfred Mertins
MOC
2010
15 years 1 months ago
Sharpness in rates of convergence for the symmetric Lanczos method
The Lanczos method is often used to solve a large and sparse symmetric matrix eigenvalue problem. There is a well-established convergence theory that produces bounds to predict the...
Ren-Cang Li
TSP
2010
15 years 1 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
CORR
2012
Springer
185views Education» more  CORR 2012»
14 years 2 months ago
Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be iden...
Johannes Textor, Maciej Liskiewicz