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» Boosting Methods for Regression
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BMCBI
2004
176views more  BMCBI 2004»
13 years 9 months ago
Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination w...
Kai Huang, Robert F. Murphy
TASLP
2010
97views more  TASLP 2010»
13 years 4 months ago
Voice Conversion Using Partial Least Squares Regression
Abstract--Voice conversion can be formulated as finding a mapping function which transforms the features of the source speaker to those of the target speaker. Gaussian mixture mode...
Elina Helander, Tuomas Virtanen, Jani Nurminen, Mo...
IPMU
2010
Springer
13 years 8 months ago
Attribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity
Abstract. In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum descr...
Kemal Ince, Frank Klawonn
ERSA
2009
387views Hardware» more  ERSA 2009»
13 years 7 months ago
Implementation of the Gauss-Newton Algorithm for Non-linear Least-mean-squares Fitting in FPGA Devices
Abstract-- The paper presents the implementation of nonlinear least-squares regression in a Field Programmable Gate Array (FPGA) device. The implemented algorithm is very performan...
Andrea Abba, Antonio Manenti, Andrea Suardi, Angel...
JMLR
2010
130views more  JMLR 2010»
13 years 4 months ago
A Regularization Approach to Nonlinear Variable Selection
In this paper we consider a regularization approach to variable selection when the regression function depends nonlinearly on a few input variables. The proposed method is based o...
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Ales...