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» Boosting Methods for Regression
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IJCV
2000
133views more  IJCV 2000»
13 years 9 months ago
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. The algor...
Yoram Leedan, Peter Meer
INCDM
2009
Springer
199views Data Mining» more  INCDM 2009»
14 years 4 months ago
Driver-Moderator Method for SKU Sales Forecasting
We develop a method to forecast stock keeping unit sales that is accurate, transparent and consistent in handling similar situations. We leverage the marketing literature to define...
Özden Gür Ali
BMCBI
2010
150views more  BMCBI 2010»
13 years 7 months ago
Kernel based methods for accelerated failure time model with ultra-high dimensional data
Background: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lp penalty have been extensively studied in survi...
Zhenqiu Liu, Dechang Chen, Ming Tan, Feng Jiang, R...
ICB
2009
Springer
140views Biometrics» more  ICB 2009»
14 years 4 months ago
A Discriminant Analysis Method for Face Recognition in Heteroscedastic Distributions
Linear discriminant analysis (LDA) is a popular method in pattern recognition and is equivalent to Bayesian method when the sample distributions of different classes are obey to t...
Zhen Lei, ShengCai Liao, Dong Yi, Rui Qin, Stan Z....
CSDA
2007
117views more  CSDA 2007»
13 years 10 months ago
Smoothing splines estimators in functional linear regression with errors-in-variables
The Total Least Squares method is generalized in the context of the functional linear model. A smoothing splines estimator of the functional coefficient of the model is first prop...
Hervé Cardot, Christophe Crambes, Alois Kne...