Formal Concept Refinement by Deep Cognitive Machine Learning

被引：0
|
作者：
Zatarain, Omar A. [1 ]
Wang, Yingxu
机构：
[1] Univ Calgary, Int Inst Cognit Informat & Cognit Comp ICIC, Lab Computat Intelligence Cognit Syst Denotat Mat, Dept Elect & Comp Engn,Schulich Sch Engn, 2500 Univ Dr NW, Calgary, AB T2N 1N4, Canada
来源：
2017 IEEE 16TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC) | 2017年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Cognitive machine learning; concept refinement; algorithm; unsupervised learning; knowledge learning; computational linguistics; cognitive computation; DENOTATIONAL MATHEMATICS; INTELLIGENT; LANGUAGE; ALGEBRA;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Concept generation and refinement is a process to generate and improve machine's knowledge base represented by a comprehensive set of formal concepts. An unsupervised algorithm for concept refinement is developed for autonomously upgrading and enhancing acquired concepts of knowledge in a cognitive knowledge base built by cognitive robots and systems. The concept refinement algorithm is implemented based on a set of rules of concept algebra and semantic analyses. Experimental results demonstrate that cognitive machines can autonomously refine their knowledge by improving acquired concepts in a dynamic process mimicking human learning mechanisms in deep machine learning and cognitive computing.
引用
收藏
页码：71 / 78
页数：8
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