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
fDate :
31 July-4 Aug. 2005
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;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556174