Study on Case-based reasoning-inspired approaches to machine-learning

被引:1
作者
Teng Zhe [1 ]
Chen Jian [1 ]
Xia Huicheng [1 ]
机构
[1] Dalian Naval Acad, Dalian 116018, Liaoning, Peoples R China
来源
2015 INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, BIG DATA AND SMART CITY (ICITBS) | 2016年
关键词
Case-based Reasoning; Machine-learning; Design; SPECIAL-ISSUE; EXPLANATION;
D O I
10.1109/ICITBS.2015.192
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This commentary briefly reviews work on the application of case-based reasoning (CBR) to the design and construction of machine-learning approaches and computer-based teaching systems. The CBR cognitive model is at the core of constructivist learning approaches such as Goal-Based Scenarios and Learning by Design. Case libraries can play roles as intelligent resources while learning and frameworks for articulating one understands. More recently, CBR techniques have been applied to design and construction of simulation-based learning systems and serious games. The main ideas of CBR are explained and pointers to relevant references are provided, both for finished work and ongoing research
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页码:760 / 763
页数:4
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