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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Early stopping of a neural network via the receiver operating curve./
作者:	Yu, Daoping.
面頁冊數:	64 p.
附註:	Source: Masters Abstracts International, Volume: 49-01, page: 0480.
Contained By:	Masters Abstracts International49-01.
標題:	Applied Mathematics. -
電子資源:	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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