NestDE: generic parameters tuning for automatic story segmentation
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- 作者：Wei Feng (1)
Xuefei Yin (1)
Yifeng Zhang (1)
Lei Xie (2)
1. Tianjin Key Laboratory of Cognitive Computing and Application ; School of Computer Science and Technology ; Tianjin University ; Tianjin ; China
2. School of Computer Science ; Northwestern Polytechnical University ; Xi鈥檃n ; China - 关键词：Generic parameters tuning ; Nested differential evolution (NestDE) ; Automatic story segmentation ; Quadratic pseudo ; Boolean optimization (QPBO)
- 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
- 出版年：2015
- 出版时间：January 2015
- 年：2015
- 卷：19
- 期：1
- 页码：61-70
- 全文大小：1,975 KB
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- 刊物主题：Numerical and Computational Methods in Engineering
Theory of Computation
Computing Methodologies
Mathematical Logic and Foundations
Control Engineering - 出版者：Springer Berlin / Heidelberg
- ISSN：1433-7479
文摘
Parameters tuning is a crucial task in automatic story segmentation. For most previous story segmentation methods, however, the parameters were simply derived from empirical tuning, which may indeed harm the fairness of the evaluation, or even misguide the conclusion. In this paper, we present a general parameters tuning approach, namely nested differential evolution. As a practical general-purpose parameters tuner, our approach itself is parameters-robust and is generic enough to optimize the most usual types of parameters for the given corpus and evaluation criterion. Besides, our approach is able to cooperate with empirical tuning and jointly produce better parameters based on the prior knowledge of experienced users. Extensive experiments on synthetic challenging quadratic pseudo-Boolean optimization and real-world story segmentation tasks validate the superior performance of our approach over traditional empirical tuning and other generic optimizers, such as simulated annealing and classical differential evolution.