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Abstract: Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We distinguish between two categories of errors in data -- random errors or `noise', and systematic errors -- and examine their relationship to the task of describing observations in a way which is also useful for helping in future problem-solving... (Update)
Context of citations to this paper: More
...of our experiments with INTEG3.1. This system is an enhanced version of INTEG.3 that has been described in an earlier paper [Brazdil and Torgo, 1990]. Section 4 discusses some other alternative methods of evaluating rule quality. This section is followed by a general...
...classified by the learned theory. The algorithm should be robust in the sense that it should cope with the various forms of noise [Brazdil Clark,1988], such as wrong attribute values, incorrect pre classification of the examples given to the algorithm, etc. Simplicity can be...
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BibTeX entry: (Update)
Brazdil, P. and Clark, P. (1990): "Learning from Imperfect Data", in Machine Learning, MetaReasoning and Logics, P. Bradzil and K. Konolige (eds.) Kluwer Academic Publishers. http://citeseer.ist.psu.edu/brazdil90learning.html More
@inproceedings{ brazdil90learning,
author = "P. Brazdil and P. Clark",
title = "Learning from imperfect data",
booktitle = "Machine Learning, Meta-Reasoning and Logics",
publisher = "Kluwer",
address = "Boston",
editor = "P. Brazdil and K. Konolige",
pages = "207--232",
year = "1990",
url = "citeseer.ist.psu.edu/brazdil90learning.html" }
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