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Abstract: This paper presents a theoretical model for learning Boolean functions in domains having a large, potentially infinite number of attributes. The model allows an algorithm to employ a rich vocabulary to describe the objects it encounters in the world without necessarily incurring time and space penalties so long as each individual object is relatively simple. We show that many of the basic Boolean functions learnable in standard theoretical models, such as conjunctions, disjunctions, K-CNF, ... (Update)
Context of citations to this paper: More
...on n. We note that in the infinite attribute model of Blum, it is possible to have algorithms whose time complexity is sublinear in n [5]. However, we do not consider that model here. If there exists a polynomial time mistake bound algorithm for learning C that makes at...
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BibTeX entry: (Update)
A. Blum. Learning boolean functions in an infinite attribute space. Machine Learning, 9:373--386, 1992. http://citeseer.ist.psu.edu/blum92learning.html More
@inproceedings{ blum90learning,
author = "A. Blum",
title = "Learning boolean functions in an infinite attribute space",
pages = "64--72",
year = "1990",
url = "citeseer.ist.psu.edu/blum92learning.html" }
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