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.5: Programs for Machine Learning , Morgan Kaufmann: San Mateo, CA. C4.5: Programs for Machine Learning Picard, R. R. and K. N. Berk. 1990. Data

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Pazzani, M. J. and D. Kibler. 1992: The utility of knowledge in inductive learning Machine Learning 9 : 57-94. The utility of knowledge in inductive learning Machine Learning

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. Machine learning models for predicting species habitat distribution suitability: An example with Pinus sylvestris L. for the Iberian Peninsula. Ecol. Model. 197: 383–393. Furlanello C

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12 149 152 Michalski, R. S., I. Bratko and M. Kubat (eds). 1998. Machine learning and data mining: Methods and Applications. Wiley, New York

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Fitzgibbon, L. J., Dowe, D. L. and Allison, L. 2002. Univariate polynomial inference by Monte Carlo message length approximation. In: C. Sammut and A. G. Hoffman (eds.) Proceedings 19 th International Conference on Machine Learning (ICML’2002), Sydney

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. Nonlinear Multivariate Analysis 1990 Globerson, A. and Tisby, N. 2003 Sufficient dimensionality reduction. J. Machine Learning

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Langley, P. 1988. Trading off simplicity and coverage in incremental concept learning. Proc. 5th Internatl. Conf. Machine Learning, Ann Arbor, Morgan Kaufman, CA. pp. 73-86. Trading off simplicity and coverage in incremental

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Conf. Machine Learning ECML-94, Catalina, Italy. Springer, Berlin, pp. 49-67. A context similarity measure 49 67

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International Conference on Machine Learning , Morgan Kaufmann, San Francisco, CA. pp. 543-550. Bayesian temporal data clustering using hidden Markov model representation 543 550

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9 43 53 Gamberger, D. and Lavra, N. 1997. Conditions for Occam's razor applicability and noise elimination. In: Proc. 9th European Conf. Machine

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