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Early stopping of a neural network v...

Yu, Daoping.

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Early stopping of a neural network via the receiver operating curve.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Early stopping of a neural network via the receiver operating curve./
Author:	Yu, Daoping.
Description:	64 p.
Notes:	Source: Masters Abstracts International, Volume: 49-01, page: 0480.
Contained By:	Masters Abstracts International49-01.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1486240
ISBN:	9781124236032

Early stopping of a neural network via the receiver operating curve.
Yu, Daoping.

Early stopping of a neural network via the receiver operating curve. - 64 p.

Source: Masters Abstracts International, Volume: 49-01, page: 0480.

Thesis (M.S.)--East Tennessee State University, 2010.

This thesis presents the area under the ROC (Receiver Operating Characteristics) curve, or abbreviated AUC, as an alternate measure for evaluating the predictive performance of ANNs (Artificial Neural Networks) classifiers. Conventionally, neural networks are trained to have total error converge to zero which may give rise to over-fitting problems. To ensure that they do not over fit the training data and then fail to generalize well in new data, it appears effective to stop training as early as possible once getting AUC sufficiently large via integrating ROC/AUC analysis into the training process. In order to reduce learning costs involving the imbalanced data set of the uneven class distribution, random sampling and k-means clustering are implemented to draw a smaller subset of representatives from the original training data set. Finally, the confidence interval for the AUC is estimated in a non-parametric approach.

ISBN: 9781124236032Subjects--Topical Terms:

1669109
Applied Mathematics.

Early stopping of a neural network via the receiver operating curve.
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008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124236032
035 $a (UMI)AAI1486240
035 $a AAI1486240
040 $a UMI $c UMI
100 1 $a Yu, Daoping. $3 1680852
245 1 0 $a Early stopping of a neural network via the receiver operating curve.
300 $a 64 p.
500 $a Source: Masters Abstracts International, Volume: 49-01, page: 0480.
500 $a Adviser: Jeff R. Knisley.
502 $a Thesis (M.S.)--East Tennessee State University, 2010.
520 $a This thesis presents the area under the ROC (Receiver Operating Characteristics) curve, or abbreviated AUC, as an alternate measure for evaluating the predictive performance of ANNs (Artificial Neural Networks) classifiers. Conventionally, neural networks are trained to have total error converge to zero which may give rise to over-fitting problems. To ensure that they do not over fit the training data and then fail to generalize well in new data, it appears effective to stop training as early as possible once getting AUC sufficiently large via integrating ROC/AUC analysis into the training process. In order to reduce learning costs involving the imbalanced data set of the uneven class distribution, random sampling and k-means clustering are implemented to draw a smaller subset of representatives from the original training data set. Finally, the confidence interval for the AUC is estimated in a non-parametric approach.
590 $a School code: 0069.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Statistics. $3 517247
650 4 $a Artificial Intelligence. $3 769149
690 $a 0364
690 $a 0463
690 $a 0800
710 2 $a East Tennessee State University. $3 1017802
773 0 $t Masters Abstracts International $g 49-01.
790 1 0 $a Knisley, Jeff R., $e advisor
790 $a 0069
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1486240