M. V. Manago and Y. Kodratoff. Noise and knowledge acquisition. In Proc. 10th Int. Joint Conf. Artificial Intelligence, pages 348--354, Milan, Italy, 1987. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Discovery of Data Evolution Regularities in Large Databases - Jiawei Han (1994) (2 citations) (Correct)
....among classes) Since relational operations are set oriented and have been implemented efficiently in many existing systems, our approach is not only efficient but easily exported to many relational systems. Our approach has absorbed many advanced features of recently developed learning algorithms [27, 18]. As shown in our study, attribute oriented induction can learn disjunctive rules and handle exceptional cases elegantly by incorporating statistical techniques in the learning process. Moreover, incremental learning has been developed in many learning algorithms [15, 28] When a new tuple is ....
M. V. Manago and Y. Kodratoff. Noise and knowledge acquisition. In Proc. 10th Int. Joint Conf. Artificial Intelligence, pages 348--354, Milan, Italy, 1987.
Learning from Imperfect Data - Pavel Brazdil (1990) (1 citation) (Correct)
....for. Such random fluctuations may be inherent in the world being observed (e.g. due to gusts of wind in a robot laboratory, imprecision in a robot arm) or in transmission of observations to the learning system (e.g. imperfect measuring equipment, transcription errors) Manago and Kodratoff [1] present a more detailed analysis of the sources of noise in data, in particular for the case where the measuring device used to relay information about the concept to the system is a human. They subdivide the category of noisy components of data into unreliable (originating from the concept ....
Michel M. Manago and Yves Kodratoff. Noise and knowledge acquisition. In J. McDermott, editor, IJCAI-87, pages 348--354, Kaufmann, Ca, 1987.
Knowledge Discovery in Databases: An Attribute-Oriented Approach - Han, Cai, Cercone (1992) (69 citations) (Correct)
....classes) Since relational operations are set oriented and have been implemented efficiently in many existing systems, our approach is not only efficient but easily exported to many relational systems. Our approach has absorbed many advanced features of recently developed learning algorithms [3, 13]. As shown in our study, attribute oriented induction can learn disjuctive rules and handle exceptional cases elegantly by incorporating statistical techniques in the learning process. Moreover, when a new tuple is inserted into a database relation, rather than restarting the learning process from ....
M. V. Manago and Y. Kodratoff, Noise and Knowledge Acquisition, Proc. 10th Int. Joint Conf. Artificial Intelligence, Milan, Italy, 1987, 348-354.
Discovery of General Knowledge in Large Spatial Databases - Wei Lu (1993) (20 citations) (Correct)
....in spatial DB adopts a learning from examples approach which treats the task relevant data as examples for learning processes and relies mainly on the generalization process. There have been many studies on machine learning [5, 6] and some recent studies on knowledge discovery in large databases [3, 7, 9, 10, 12, 16]. These studies set up the foundation for knowledge discovery in spatial databases. Recently, an attribute oriented approach has been developed for discovery of different kinds of knowledge rules in relational databases [9] Moreover, a multi resolution relational data model has been developed ....
M. V. Manago and Y. Kodratoff, Noise and Knowledge Acquisition, Proc. 10th Int. Joint Conf. Artificial Intelligence, Milan, Italy, 1987, 348-354.
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