Four stances on knowledge acquisition and machine learning

1991 ◽  
pp. 514-533 ◽  
Author(s):  
T. R. Addis ◽  
Y. Kodratoff ◽  
R. L de Mantaras ◽  
K. Morik ◽  
E. Plaza
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition

Machine Learning as a Knowledge Acquisition Tool Application in the Domain of the Interpretation of Test Results

1989 ◽  
pp. 37-44
Author(s):  
R. A. J. Schijven ◽  
J. L. Talmon ◽  
E. Ermers ◽  
R. Penders ◽  
P. J. E. H. M. Kitslaar
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Test Results

Machine Learning and Knowledge Acquisition

1992 ◽  
Vol 5 (1) ◽  
pp. 19-24 ◽  
Author(s):  
F. Bergadano ◽  
Y. Kodratoff ◽  
K. Morik
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition

On a Novel Cognitive Knowledge Base (CKB) for Cognitive Robots and Machine Learning

2014 ◽  
Vol 6 (2) ◽  
pp. 41-62 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Cognitive Learning ◽  
Knowledge Bases ◽  
Formal Concept ◽  
Human Knowledge ◽  
Cognitive Knowledge ◽  
Cognitive Robots ◽  
Concept Network

A cognitive knowledge base (CKB) is a novel structure of intelligent knowledge base that represents and manipulates knowledge as a dynamic concept network mimicking human knowledge processing. The essence of CKB is the denotational mathematical model of formal concept that is dynamically associated to other concepts in a CKB beyond conventional rule-based or ontology-based knowledge bases. This paper presents a formal CKB and autonomous knowledge manipulation system based on recent advances in neuroinformatics, concept algebra, semantic algebra, and cognitive computing. An item knowledge in CKB is represented by a formal concept, while the entire knowledge base is embodied by a dynamic concept network. The CKB system is manipulated by algorithms of knowledge acquisition and retrieval on the basis of concept algebra. CKB serves as a kernel of cognitive learning engines for cognitive robots and machine learning systems. CKB plays a central role not only in explaining the mechanisms of human knowledge acquisition and learning, but also in the development of cognitive robots, cognitive learning engines, and knowledge-based systems.

What online machine learning can do for knowledge acquisition—a case study

1994 ◽  
Vol 6 (4) ◽  
pp. 435-460 ◽  
Author(s):  
Edgar Sommer ◽  
Katharina Morik ◽  
Jean-Michel André ◽  
Marc Uszynski
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition

A formal analysis of machine learning systems for knowledge acquisition

1988 ◽  
Vol 29 (4) ◽  
pp. 429-446 ◽  
Author(s):  
Valerie L. Shalin ◽  
Edward J. Wisniewski ◽  
Keith R. Levi ◽  
Paul D. Scott
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Formal Analysis ◽  
Learning Systems

MACHINE LEARNING FOR OBJECT RECOGNITION AND SCENE ANALYSIS

1994 ◽  
Vol 08 (01) ◽  
pp. 259-304 ◽  
Author(s):  
Y. KODRATOFF ◽  
S. MOSCATELLI
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Knowledge Acquisition ◽  
Image Understanding ◽  
Research Field ◽  
Learning System ◽  
Model Matching ◽  
Vision Systems ◽  
Recent Developments ◽  
Optimizing Model

Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptive target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented on and its strong and weak points will be underlined. In conclusion we will suggest what could be the “ideal” learning system for vision.

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