Brian Gaines and Paul Compton. Induction of ripple-down rules applied to modeling large databases. JIIS, 5(3), 1993. Home/Search Document Details and Download
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Interactive Machine Learning - Ware, Frank, Holmes, Hall, Witten (2000) (Correct)
....For large datasets involving high dimensional interactions, manual classi er construction is too tedious to be worthwhile. The new challenge is to create a symbiotic relationship that combines the skills of human user and machine learning algorithm. For example, learning algorithms like Induct (Gaines Compton, 1995), which produces a hierarchy of if then rules, lend 3 The software is part of the Weka workbench, available from http: www.cs.waikato.ac.nz ml 12 INTERACTIVE MACHINE LEARNING themselves naturally to a symbiotic approach. Situations in which manual decisiontree construction will fail can be ....
Gaines, B. & Compton, P. (1995). Induction of ripple-down rules applied to modeling large databases. Journal of Intelligent Information Systems, 5(3), 211-228.
The Reuse of Knowledge: Research, Issues and the Ripple-Down.. - Richards (Correct)
....has not been necessary. However, the study also found that it was necessary to generate additional knowledge in the form of higher level concepts using FCA to support activity reuse. At this time no work has been done on combining RDR KBS components with other type of KBS although the KSSn tool (Gaines and Compton 1995) does allow RDR rules to be generated using the machine learning algorithm INDUCT. Catlett (1992) has also recommended that RDR be used as a mediating representation as it is executable as well as comprehensible to experts. 11 4 An Alternative View of Reuse Many of the reuse approaches ....
Gaines, B.R. and Compton, P. (1995) Induction of Ripple-Down Rules Applied to Modeling Large Databases, Journal for Intelligent Information Systems 5(3): 211-228.
Knowledge Acquisition, Modeling and Inference through the.. - Gaines, Shaw Self-citation (Gaines) (Correct)
.... and the dataset and its proper inductive model were originally developed by Cendrowska (1987) 21 Figure 20 Status screen for contact lens data set 22 When the Induct button in Figure 20 is clicked the inference rules shown in Figure 21 are returned, in this example as a pair of EDAGs (Gaines, 1995), one each for the two conclusion constructs specified. The interpretation of the first EDAG is that by default one should prescribe not soft unless tear production is normal and the client is not astigmatic when one should prescribe soft unless the client is also presbyopic and myope ....
....unless tear production is normal and the client is astigmatic and either the client is myope or young when one should prescribe hard. The controls at the bottom of Figure 21 allow the user to specify that the induction module in WebGrid produce simple production rules or ripple down rules (Gaines and Compton, 1995), but usually the EDAGs are the most compact knowledge representation. Figure 21 Inference rules returned by Induct When the Test button in Figure 20 is clicked the test case screen shown in Figure 22 is returned. This is familiar to the user because it is very similar to the screen for ....
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Gaines, B.R. and Compton, P. (1995). Induction of ripple-down rules applied to modeling large databases. Journal for Intelligent Information Systems 5(3) 211-228.
Geometric and Combinatorial Tiles in 0-1 Data - Gionis, Mannila, Seppänen (2004) (Correct)
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Brian Gaines and Paul Compton. Induction of ripple-down rules applied to modeling large databases. JIIS, 5(3), 1993.
Context Dynamic and Explanation in Contextual Graphs - Brézillon (2003) (Correct)
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Gaines, B.R., Compton, P.: Induction of Ripple-Down rules applied to modeling large databases. Journal of Intelligent Information Systems, 5(3) (1995) 211--228
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