Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Boosting through optimization of margin distributions
Author: Shen, C.
Li, H.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2010; 21(4):659-666
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2010
ISSN: 1045-9227
Abstract: Boosting has been of great interest recently in the machine learning community because of the impressive performance for classification and regression problems. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently, it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data into account. Most of the current boosting algorithms in practice usually optimize a convex loss function and do not make use of the margin distribution. In this brief, we design a new boosting algorithm, termed margin-distribution boosting (MDBoost), which directly maximizes the average margin and minimizes the margin variance at the same time. This way the margin distribution is optimized. A totally corrective optimization algorithm based on column generation is proposed to implement MDBoost. Experiments on various data sets show that MDBoost outperforms AdaBoost and LPBoost in most cases.
Keywords: Humans
Computer Simulation
Electronic Data Processing
Generalization, Psychological
Neural Networks, Computer
Rights: © Copyright 2010 IEEE – All Rights Reserved
DOI: 10.1109/TNN.2010.2040484
Appears in Collections:Aurora harvest 5
Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.