Abstract
Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert’s analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learning method.
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References
Rendell, L. and Seshu, R. (1990). Learning hard concepts through constructive induction. Computational Intelligence, 6 (pp. 247–270 ).
Minsky, M. and Papert, S. (1969). Perceptrons. Cambridge, Mass.: MIT Press.
Minsky, M. and Papert, S. (1988). Perceptrons: An Introduction to Computational Geometry (expanded edn). Cambridge, Mass.: MIT Press.
Thornton, C. (1996). Parity: the problem that won’t go away. In G. McCalla (Ed.), Proceeding of AI-96 (Toronto, Canada) (pp. 362–374). Springer.
Holte, R. (1993). Very simple classification rules perform well on most commonly used datasets. Machine learning, 3 (pp. 63–91 ).
Fisher, D. and McKusick, K. (1989). An empirical comparison of ID3 and back-propagation. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 788–793). Morgan Kaufmann.
Muggleton, S. (Ed.) (1992). Inductive Logic Programming. Academic Press.
Holland, J. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
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© 1998 Springer-Verlag London Limited
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Thornton, C. (1998). Separability is a Learner’s Best Friend. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_4
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DOI: https://doi.org/10.1007/978-1-4471-1546-5_4
Publisher Name: Springer, London
Print ISBN: 978-3-540-76208-9
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