NDLI: Input decay: simple and effective soft variable selection

Content Provider	IEEE Xplore Digital Library
Author	Chapados, N. Bengio, Y.
Copyright Year	2001
Description	Author affiliation: Dept. of Comput. Sci. & Oper. Res., Montreal Univ., Que., Canada (Chapados, N.)
Abstract	To deal with the overfitting problems that occur when there are not enough examples compared to the number of input variables in supervised learning, traditional approaches are weight decay and greedy variable selection. An alternative that has recently started to attract attention is to keep all the variables but to put more emphasis on the "most useful" ones. We introduce a new regularization method called input decay that exerts more relative penalty on the parameters associated with the inputs that contribute less to the learned function. This method, like weight decay and variable selection, still requires to perform a kind of model selection. Successful comparative experiments with this new method were performed both on a simulated regression task and a real-world financial prediction task.
Sponsorship	Int. Neural Network Soc.
Starting Page	1233
Ending Page	1237
File Size	444810
Page Count	5
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939537
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Input variables Predictive models Neural networks Linear regression Computer science Operations research Supervised learning Machine learning algorithms Computer networks Computational modeling
Content Type	Text
Resource Type	Article

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