NDLI: Smoothed Bagging with Kernel Bandwidth Selectors

Content Provider	Springer Nature Link
Author	Lee, Shinjae Cho, Sungzoon
Copyright Year	2001
Abstract	Recently, a combined approach of bagging (bootstrap aggregating) and noise addition was proposed and shown to result in a significantly improved generalization performance. But, the level of noise introduced, a crucial factor, was determined by trial and error. The procedure is not only ad hoc but also time consuming since bagging involves training a committee of networks. Here we propose a principled procedure of computing the level of noise, which is also computationally less expensive. The idea comes from kernel density estimation (KDE), a non-parametric probability density estimation method where appropriate kernel functions such as Gaussian are imposed on data. The kernel bandwidth selector is a numerical method for finding the width of a kernel function (called bandwidth). The computed bandwidth can be used as the variance of added noise. The proposed approach makes the trial and error procedure unnecessary, and thus provides a much faster way of finding an appropriate level of noise. In addition, experimental results show that the proposed approach results in an improved performance over bagging, particularly for noisy data.
Starting Page	157
Ending Page	168
Page Count	12
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	14
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2001-01-01
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Artificial Intelligence (incl. Robotics) Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks Electronic and Computer Engineering Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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