SELF-ORGANIZING NETWORKS FOR EXTRACTING JET FEATURES

被引：13

作者：

LONNBLAD, L

PETERSON, C

PI, H

ROGNVALDSSON, T

机构：

[1] Department of Theoretical Physics, University of Lund, S-22362 Lund

来源：

COMPUTER PHYSICS COMMUNICATIONS | 1991年 / 67卷 / 02期

关键词：

D O I：

10.1016/0010-4655(91)90016-E

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics: hadronization model discrimination and separation of b, c and light quarks.

引用

页码：193 / 209

页数：17

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