NestDE: generic parameters tuning for automatic story segmentation
| LEADER | caa a22 4500 | ||
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| 001 | 605468370 | ||
| 003 | CHVBK | ||
| 005 | 20210128100316.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1450-2 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1450-2 | ||
| 245 | 0 | 0 | |a NestDE: generic parameters tuning for automatic story segmentation |h [Elektronische Daten] |c [Wei Feng, Xuefei Yin, Yifeng Zhang, Lei Xie] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Generic parameters tuning |2 nationallicence | |
| 690 | 7 | |a Nested differential evolution (NestDE) |2 nationallicence | |
| 690 | 7 | |a Automatic story segmentation |2 nationallicence | |
| 690 | 7 | |a Quadratic pseudo-Boolean optimization (QPBO) |2 nationallicence | |
| 700 | 1 | |a Feng |D Wei |u Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China |4 aut | |
| 700 | 1 | |a Yin |D Xuefei |u Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China |4 aut | |
| 700 | 1 | |a Zhang |D Yifeng |u Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China |4 aut | |
| 700 | 1 | |a Xie |D Lei |u School of Computer Science, Northwestern Polytechnical University, Xi'an, China |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/1(2015-01-01), 61-70 |x 1432-7643 |q 19:1<61 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1450-2 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1450-2 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Feng |D Wei |u Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Yin |D Xuefei |u Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhang |D Yifeng |u Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Xie |D Lei |u School of Computer Science, Northwestern Polytechnical University, Xi'an, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/1(2015-01-01), 61-70 |x 1432-7643 |q 19:1<61 |1 2015 |2 19 |o 500 | ||