Oates, T., & Jensen, D. (1997). The eects of training set size on decision tree complexity. In Fisher, D. H. (Ed.), Proceedings of the Fourteenth International Conference on Machine Learning, pp. 254261, San Francisco, CA. Morgan Kaufmann. Home/Search Document Not in Database Summary Related Articles Check |
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An Analysis of Reduced Error Pruning - Elomaa, Kääriäinen (2001) (1 citation) (Correct)
....dependency on the training data. Pruning aims at removing from the tree those parts that are likely to only be due to the chance properties of the training set. The problems of the two phased top down induction of decision trees are well known and have been extensively reported (Catlett, 1991; Oates Jensen, 1997, 1998) The size c 2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved. Elomaa Kriinen of the tree grows linearly with the size of the training set, even though after a while no accuracy is gained through the increased tree complexity. Obviously, pruning is intended ....
....processed in a bottom up manner, since subtrees must be checked for the same property before pruning a node but, on the other hand, the last quotation would indicate rep to be an iterative method. We take rep to have the following single scan bottom up control strategy like in most other studies (Oates Jensen, 1997, 1998, 1999; Esposito et al. 1993, 1997; Kearns Mansour, 1998) Nodes are pruned in a single bottom up sweep through the decision tree, pruning each node is considered as it is encountered. The nodes are processed in postorder. By this order of node processing, any tree that is a candidate ....
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Oates, T., & Jensen, D. (1997). The eects of training set size on decision tree complexity. In Fisher, D. H. (Ed.), Proceedings of the Fourteenth International Conference on Machine Learning, pp. 254261, San Francisco, CA. Morgan Kaufmann.
E4 - Machine Learning - Domingos (Correct)
.... Moore, 1999) The sampling and summarization approaches are complementary and can be used together (e.g. Bradley, Fayyad, Reina, 1998) For many problems, large quantities of data may be available, but may not be necessary to learn the desired concepts to the required level of accuracy (Oates Jensen, 1997). For others, even the large quantities available may not be su#cient to capture all the relevant structure. It would thus be useful to have methods, even if heuristic in nature, to estimate early on how much data will be needed. Examples of research in this direction are the fitting of power laws ....
Oates, T., & Jensen, D. (1997). The e#ects of training set size on decision tree complexity. In Proceedings of the Fourteenth International Conference on Machine Learning (pp. 254-262).
An Empirical Comparison of Voting Classification Algorithms.. - Bauer, Kohavi (1998) (153 citations) (Correct)
....bootstrap samples for a given dataset was always smaller than the corresponding size of the trees generated by MC4 alone. We postulate that this e#ect is due to the smaller e#ective size of training sets under bagging, which contain only about 63.2 unique instances from the original training set. Oates Jensen (1997) have shown that there is a close correlation between the training set size and the tree complexity for the reduced error pruning algorithm used in C4.5 and MC4. The trees generated from the bootstrap samples were initially grown to be smaller than the corresponding MC4 trees, yet they were larger ....
Oates, T. & Jensen, D. (1997), The e#ects of training set size on decision tree complexity, in D. Fisher, ed., `Machine Learning: Proceedings of the Fourteenth International Conference', Morgan Kaufmann, pp. 254--262.
Learning Planning Operators in Real-World, Partially.. - Schmill, Oates, Cohen (2000) (2 citations) Self-citation (Oates) (Correct)
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Tim Oates and David Jensen. The e ects of training set size on decision tree complexity. In Proceedings of the Fourteenth International Conference on Machine Learning, 1997.
Automatic Construction of Decision Trees from Data: A.. - Murthy (1997) (37 citations) (Correct)
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Tim Oates and David Jensen. The e#ects of training set size on decision tree complexity. In Proceedings of the 14th International Conference on Machine Learning, pages 254#262. Morgan Kaufmann, 1997.
A Comparison of Prediction Accuracy, Complexity, and Training.. - Lim, LOH, al. (2000) (3 citations) (Correct)
No context found.
T. Oates and D. Jensen. The e ects of training set size on decision tree complexity. In D. H. Fisher, Jr., editor, Proceedings of the Fourteenth International Conference on Machine Learning, pages 254-262, San Francisco, CA, 1997. Morgan Kaufmann.
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