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KNOWLEDGE-TRANSFER-BASED LEARNING FRAMEWORK FOR AIRSPACE SITUATION EVALUATION

机译:基于知识转移的学习框架在空间状况评估中的应用

摘要

A sector situation (SS) evaluation framework is based on knowledge transfer and is specifically applicable for small-training-sample environment. The SS evaluation framework is able to effectively mine knowledge hidden within the samples of both target and non-target sectors, and properly handle the integration between the knowledge derived from different sectors. This framework includes three main steps: (1) sufficiently mine the knowledge within the samples of the target sector using the strategies of multi-factor subset generation and multi-base evaluator construction, and build target base evaluators; (2) precisely learn the knowledge in the samples of the non-target sectors using similar strategies for the target sector, together with a sample transformation, and build non-target base evaluators; and (3) efficiently integrate the target and non-target base evaluators based on evaluation confidence analysis of those base evaluators.
机译:部门情况(SS)评估框架基于知识转移,并且特别适用于小型培训样本环境。 SS评估框架能够有效地挖掘隐藏在目标和非目标部门样本中的知识,并正确处理来自不同部门的知识之间的整合。该框架包括三个主要步骤:(1)使用多因素子集生成和多基数评估器构建策略充分挖掘目标部门样本中的知识,并建立目标基数评估器; (2)使用类似的目标行业策略,精确学习非目标行业样本中的知识,并进行样本转换,并建立非目标基础评估者; (3)根据对基础评估者的评估置信度分析,有效地整合目标评估者和非目标基础评估者。

著录项

  • 公开/公告号US2019138947A1

    专利类型

  • 公开/公告日2019-05-09

    原文格式PDF

  • 申请/专利权人 BEIHANG UNIVERSITY;

    申请/专利号US201816179853

  • 发明设计人 XIANBIN CAO;WENBO DU;XI ZHU;YUMENG LI;

    申请日2018-11-02

  • 分类号G06N99;G06F17/16;

  • 国家 US

  • 入库时间 2022-08-21 12:04:54

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