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JMLR
2008
151views more  JMLR 2008»
15 years 2 months ago
Learning to Combine Motor Primitives Via Greedy Additive Regression
The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Addit...
Manu Chhabra, Robert A. Jacobs
MA
2010
Springer
135views Communications» more  MA 2010»
15 years 1 months ago
Nonparametric comparison of regression functions
In this work we provide a new methodology for comparing regression functions m1 and m2 from two samples. Since apart from smoothness no other (parametric) assumptions are required...
Ramidha Srihera, Winfried Stute
TSP
2010
14 years 9 months ago
Distributed sparse linear regression
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems....
Gonzalo Mateos, Juan Andrés Bazerque, Georg...
BMCBI
2011
14 years 6 months ago
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) ...
Alfredo A. Kalaitzis, Neil D. Lawrence
MIR
2010
ACM
217views Multimedia» more  MIR 2010»
15 years 8 months ago
Feature selection for content-based, time-varying musical emotion regression
In developing automated systems to recognize the emotional content of music, we are faced with a problem spanning two disparate domains: the space of human emotions and the acoust...
Erik M. Schmidt, Douglas Turnbull, Youngmoo E. Kim