Feature learning, multiresolution analysis, and symbol grounding
Feature learning, multiresolution analysis, and symbol grounding
复制标题
特征学习、多分辨率分析和符号基础
DOI：
10.1017/s0140525x98420107
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发表时间：
1998
影响因子：
29.3
通讯作者：
K. Macdorman
中科院分区：
文献类型：
--
作者：
K. Macdorman
基于固定特征集的认知理论存在框架和符号基础问题。灵活的特征和其他凭经验获得的约束（例如，模拟到模拟映射）提供了一种用于让外部关系影响符号操作的框架。通过提供一个生物学上合理的基础上的功能学习，非正交多分辨率分析和降维，通知功能约束，可能有助于解决符号接地问题。
Cognitive theories based on a fixed feature set suffer from frame and symbol grounding problems. Flexible features and other empirically acquired constraints (e.g., analog-to-analog mappings) provide a framework for letting extrinsic relations influence symbol manipulation. By offering a biologically plausible basis for feature learning, nonorthogonal multiresolution analysis and dimensionality reduction, informed by functional constraints, may contribute to a solution to the symbol grounding problem.