DocumentCode
446035
Title
Third-order generalization and a new approach to systematically categorizing higher-order generalization
Author
Neville, Richard S.
Author_Institution
Sch. of Informatics, Manchester Univ., UK
Volume
3
fYear
2005
fDate
31 July-4 Aug. 2005
Firstpage
1924
Abstract
Higher-order generalization is a means of categorizing different types of generalization. The paper presents a framework within which higher-order generalization can be evaluated in a detailed and systematic way. Previous research divided generalization into three categories. However, these categories were fuzzy and imprecise. This paper further refines existing definitions by first assigning each category a logical predicate that it must fulfil in order to achieve a specific order (type) of generalization. Then, it breaks the orders down into four different categories in a detailed and systematic way. The paper focuses on early (initial) results; some of the aims have been demonstrated and amplified through the experimental work.
Keywords
generalisation (artificial intelligence); higher-order generalization; logical predicate; third-order generalization; Artificial neural networks; Equations; Informatics; Network topology; Neurons; Optimization methods; Phase estimation; Probability distribution; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN
0-7803-9048-2
Type
conf
DOI
10.1109/IJCNN.2005.1556174
Filename
1556174
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