Gen89. Gennari, J.H., Langley, P., and Fisher, D.H. Models of Incremental Concept Formation. Artificial Intelligence , 40 (1989), 11-61. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Documents 51 to 100 Previous 50 Next 50
Hierarchical Clustering of Composite Objects with a Variable.. - Ketterlin Gan (1995) (4 citations) (Correct)
....hierarchies. The object is sorted down through the current hierarchy. At each level, several operators are temptatively applied to the current partition, some of them having restructuring properties. The best of these operators is definitely applied, and the process restarts one level deeper (see [3, 5] for a complete description of the algorithm) When several distinct partitions are generated, a heuristic called category utility is used to select between them. Category utility evaluates the global quality of a single partition. This evaluation is based on the individual predictivity of each ....
.... individual predictivity of a concept is defined as: Pi(C ) 1 I I X i=1 Pi(A i ; C ) where Pi(A i ; C ) quantifies how the attribute 1 In this paper, we consider Cobweb to be the algorithm described in [3] with an extension to deal with numerical attributes developpedin Classit(see [5]) but without any other extension from Classit. A i is predictive in C (i.e. how precisely values of A i can be predicted for members of C ) In this framework, an learning problem is defined on a set of attributes. Observations must be represented by a value for all (or some) of the ....
[Article contains additional citation context not shown here]
Gennari, J.H., Langley, P., & Fisher, D.H. (1989). Models of Incremental Concept Formation. Artificial Intelligence, 40, pp. 11--61.
Conceptual Clustering with Numeric-and-Nominal Mixed Data - A.. - Li, Biswas (Correct)
....of correct predictions given no class information. The partition score, i.e. the utility of a partition structure made up of K classes, is defined as the average CU over the K classes: P K k=1 CUk K . COBWEB 3 combines the original COBWEB[6] algorithm with the methodology defined in CLASSIT[11] to handle numeric attributes in the CU measure. For numeric attributes, probabilities are expressed in terms of the probability density function(pdf) defined for the range of values that can be associated with the attribute. COBWEB 3 assumes that the numeric feature values are normally ....
J.H. Gennari, P. Langley, and D.H. Fisher. "Models of Incremental Concept Formation", in Artificial Intelligence, Vol. 40, pp. 11-61, 1989.
Incremental Conceptual Clustering Without Order Dependency - Altintas (1995) (Correct)
....as it is given new data. Incremental learning leads naturally to the integration of learning with performance. In any incremental system action by the performance component drives the learning element. In contrast, nonincremental systems isolate the processes of learning and performance [8]. Conceptual clustering can be considered as a search through a space of concept hierarchies. Especially for incremental systems, hill climbing is a possible method for controlling that search. In incremental hill climbing systems, each step through the hypothesis space occurs in response to some ....
....the precursors of the concept hierarchies. It introduced the two learning mechanisms of discrimination and familiarization, but the selection of attributes for these purposes was ad hoc. Since the system did not state a concept description for the internal nodes, a concept hierarchy is not formed [8]. Lebowitz s UNIMEM [15] was the successor of EPAM but it was within the framework of generalization based memory although the author treates it as a conceptual clustering system. UNIMEM used hill climbing search for concept formation and produced a true concept hierarchy with descriptions at each ....
[Article contains additional citation context not shown here]
Gennari, John H., Langley, Pat & Fisher, Doug, Models of incremental concept formation, Artificial Intelligence, Vol. 40, pp. 11-61, 1989.
ML experiments with an accuracy-efficiency trade off in.. - McEwen (1996) (1 citation) (Correct)
....from a sequence of presented instances. This task is very much like that of conceptual clustering, indeed the main difference between the two is that concept formation systems are rather more rigidly defined and constrained than conceptual clustering systems are. Gennari, Langley Fisher [4] identify several features which characterise concept formation systems, these features do not constitute a formal definition as such but are simply features that concept formation systems are likely to possess, or rather systems that possess these features are likely to be termed concept ....
J. H. Gennari, P. Langley, and D. Fisher. Models of incremental concept formation. Artificial Intelligence, (40):11--61, 1989.
Using Dia-MoLE For Unsupervised Learning Of Domain-Specific.. - Möller (Correct)
No context found.
Gen89. Gennari, J.H., Langley, P., and Fisher, D.H. Models of Incremental Concept Formation. Artificial Intelligence , 40 (1989), 11-61.
Thematic Image Segmentation by a Concept Formation Algorithm - Korczak Blamont (1994) (Correct)
No context found.
Gennari, J.H., Langley, P., & Fisher, D.H. (1989). Models of Incremental Concept Formation. Artificial Intelligence, 40, pp. 11-61.
Documents 51 to 100 Previous 50 Next 50
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC