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Analyses of instance-based learning algorithms

Albert, M.K. and Aha, D.W. (1991) Analyses of instance-based learning algorithms In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91). AAAI., pp. 553-558.

Record type: Conference or Workshop Item (Paper)


This paper presents PAC-learning analyses for instance-based learning algorithms for both symbolic and numeric-prediction tasks. The algorithms analyzed employ a variant of the k-nearest neighbor pattern classifier. The main results of these analyses are that the IB1 instance-based learning algorithm can learn, using a polynomial number of instances, a wide range of symbolic concepts and numeric functions. In addition, we show that a bound on the degree of difficulty of predicting symbolic values may be obtained by considering the size of the boundary of the target concept, and a bound on the degree of difficulty in predicting numeric values may be obtained by considering the maximum absolute value of the slope between instances in the instance space. Moreover, the number of training instances required by IBl is polynomial in these parameters. The implications of these results for the practical application of instance-based learning algorithms are discussed.

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Published date: 1991
Venue - Dates: Ninth National Conference on Artificial Intelligence, 1991-07-14 - 1991-07-19


Local EPrints ID: 18497
ISBN: 9780262510592
PURE UUID: 4befab13-9e1b-41ef-873e-5e56d2423fe5

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Date deposited: 27 Jul 2006
Last modified: 17 Jul 2017 16:35

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Author: M.K. Albert
Author: D.W. Aha

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