M. Manago. Knowledge intensive induction. In A. M. Segre, editor, Proceedings of the 6th International Workshop on Machine Learning, pages 151-155, Ithaca, 1989. Morgan Kaufmann. Home/Search Document Not in Database Summary Related Articles Check |
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Heterogeneous Uncertainty Sampling for Supervised Learning - Lewis, Catlett (1994) (70 citations) (Correct)
....large text databases because it requires that training and test instances be presented as tuples specifying the values of all attributes. With 319,463 training instances and 67,331 attributes this would have required over 40 gigabytes. The extravagance of expanding such spase data was stressed in [22]. The C4.5 algorithm could be implemented in a manner suited to sparse data, but almost no machine learning software has this feature. Even eliminating attributes that take on the value True less than five times in the training data would still have left 24,052 attributes, at the cost of ....
Michel Manago. Knowledge intensive induction. In Machine Learning: Proceedings of the Sixth International Workshop, pages 151--155, 1989.
Structural Regression Trees - Kramer (1996) (26 citations) (Correct)
....have been developed for CART, since the method grows the tree until every leaf is pure , i.e. the leaf covers exactly one instance. The regression tree resulting from the growing phase is usually bigger than a classification tree, since it takes more nodes to achieve pure leaves. Manago s KATE [17] learns decision trees from examples represented in a frame based language that is equivalent to first order predicate calculus. KATE makes extensive use of a given hierarchy and heuristics to generate the branch tests. To our knowledge, KATE was the first system to induce first order theories in ....
M. Manago, `Knowledge-intensive induction', in Proceedings of the Sixth International Workshop on Machine Learning, ed., A.M. Segre, pp. 151-- 155. Morgan Kaufman, (1989).
Learning of Class Descriptions from Class.. - Geibel, Schädler.. (2002) (Correct)
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M. Manago. Knowledge intensive induction. In A. M. Segre, editor, Proceedings of the 6th International Workshop on Machine Learning, pages 151-155, Ithaca, 1989. Morgan Kaufmann.
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