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ICANN
2003
Springer
14 years 1 months ago
A Comparison of Model Aggregation Methods for Regression
Combining machine learning models is a means of improving overall accuracy.Various algorithms have been proposed to create aggregate models from other models, and two popular examp...
Zafer Barutçuoglu
JMLR
2010
110views more  JMLR 2010»
13 years 2 months ago
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
A new approach to regression regularization called the Pairwise Elastic Net is proposed. Like the Elastic Net, it simultaneously performs automatic variable selection and continuo...
Alexander Lorbert, David Eis, Victoria Kostina, Da...
EUROGP
2001
Springer
105views Optimization» more  EUROGP 2001»
14 years 8 days ago
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
Abstract. In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm o...
Jeroen Eggermont, Jano I. van Hemert
ICMCS
2007
IEEE
126views Multimedia» more  ICMCS 2007»
14 years 2 months ago
Music Emotion Classification: A Regression Approach
Typical music emotion classification (MEC) approaches categorize emotions and apply pattern recognition methods to train a classifier. However, categorized emotions are too ambigu...
Yi-Hsuan Yang, Yu-Ching Lin, Ya-Fan Su, Homer H. C...
CSDA
2007
128views more  CSDA 2007»
13 years 7 months ago
Regularized linear and kernel redundancy analysis
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures r...
Yoshio Takane, Heungsun Hwang