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

页码：71 / 78

页数：8

共 50 条

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