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Abstract: In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior---thus, the same input may sometimes be classified as a positive example and sometimes as a negative example. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. We adopt from the ... (Update)
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.... this data, the learner is required to come up with a procedure that, given any unclassified instance, returns a confidence value [SS98, KS90] in the range ### ## that the given instance is in the concept. To simplify our treatment we assume that the instance space is #...
.... capacity measures are defined in the theory, the most popular one being the VC dimension [10] or scale sensitive versions of it [11], 12] For more details and examples of exact forms of , we refer the reader to [10] 4] and [12] Intuitively, if the capacity of the...
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BibTeX entry: (Update)
M. J. Kearns and R. E. Schapire, "Efficient distribution-free learning of probabilistic concepts," in 31st Annual IEEE Symposium on Foundations of Computer Science, pp. 382--391, 1990. http://citeseer.ist.psu.edu/article/kearns93efficient.html More
@incollection{ kearns94efficient,
author = "Kearns and Schapire",
title = "Efficient Distribution-free Learning of Probabilistic Concepts",
booktitle = "Computational Learning Theory and Natural Learning Systems, Volume I: Constraints and Prospect, edited by Stephen Jose Hanson, George A. Drastal, and Ronald L. Rivest, Bradford/{MIT} Press",
volume = "1",
year = "1994",
url = "citeseer.ist.psu.edu/article/kearns93efficient.html" }
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