Abstract
Pattern recognition has for decades played an important role in the development of intelligent systems and numerous algorithms have been proposed to deal with this important task. Though much interest and research has been invested into the construction of advanced pattern recognition systems, almost all of these algorithms are incapable of meta-learning. These one-shot learning systems do not address the problem of automatic adaptation of learning bias and hence lack one of the most fundamental requirements inherent to any truly autonomous pattern recognition system: the ability to learn to learn. The purpose of this paper is to report the findings of an extensive empirical study (i.e., including 26 real-world data sets) involving a system capable of meta-learning. Statistically significant reductions in both network units and training time are observed when utilizing a multi-shot learning environment.
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References
Baffes, P.T. and Zelle, J.: Growing Layers of Perceptrons: Introducing the Extentron Algorithm, Proceedings of the 1992 International Joint Conference on Neural Networks (pp. II-392–II-397), Baltimore, MD., June, 1992.
Belew, R.K., Mclnerney, J., and Schraudolph, N.N.: Evolving Network: using the genetic algorithm with connectionist learning, In C.G. Langton, C. Taylor, J.D. Farmer and S. Rasmussen (Eds.) Artificial Life I I, Redwood City, CA: Addison-Wesley, 1991.
Bellgard, M. I., Tsang, C. P.: Some Experiments on the Use of Genetic Algorithms in a Boltzmann Machine. International Joint Conference on Neural Networks, pp. 2645–2652, Singapore, 1991.
Fahlman, S.E. and Lebiere, C.: The Cascade-Correlation Learning Architecture, In D. Touretzky (Ed.), Advances in Neural Information Processing Systems 2 (pp. 524–532 ). San Mateo, CA.: Morgan Kaufmann, 1990.
Fogel, D.B., Fogel, L.J., and Porto, V.W.: Evolving Neural Networks, Biological Cybernetics, 63, 487–493, 1990.
Frean, M.: The Upstart Algorithm: A Method for Constructing and Training FeedForward Neural Networks, Neural Computation, 2, 198–209, 1991.
Marti, L.: Genetically Generated Neural Networks II: Search for an Optimal Representation, IJCNN92, Vol. II, Baltimore, June, 1992.
Miller, G.F., Todd, P.M., and Hedge, S.U.: Designing neural networks using genetic algorithms, In J.D. SchafFer (Ed.) 3rd ICGA, San Mateo, CAMorgan Kaufmann, 1989.
Romaniuk, S.G.: Evolutionary Growth Perceptrons, In S. Forrest (Ed.) Genetic Algorithms: Proceedings of the 5th International Conference, Morgan Kaufmann, 1993.
Romaniuk, S.G.: Trans-Dimensional Learning, International Journal of Neural Systems, Vol. 4, No. 2 (June), 171–185, 1993.
Romaniuk, S.G.: Pruning Divide & Conquer Networks, Network: Computation in Neural Systems, 4, 481–494, 1993.
Romaniuk, S.G.: Learning to Learn: Automatic Adaptation of Learning Bias, AAAI-94, Seattle, WA, USA, 1994.
Romaniuk, S.G.: Learning to Learn Real-World Pattern Recognition Problems, Unpublished report, Available from author, 1995.
Romaniuk, S.G.: Learning to Forget, Unpublished report, Available from author, 1995.
Romaniuk, S. G.: Semi-Weighted Neural Networks, Unpublished report, Available from author, 1995.
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Romaniuk, S.G. (1995). Application of Learning to Learn to Real-World Pattern Recognition. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_53
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_53
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive