Utgoff, P. E. (1989). Improved training via incremental learning. Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann. Home/Search Document Not in Database Summary Related Articles Check |
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Information Filtering: Selection Mechanisms In Learning Systems - Markovitch (1989) (25 citations) (Correct)
....shows the proportion of the selected experiences that DIDO predicts incorrectly. The error rate does not decline until the last stage of the learning, proving that the selection procedure indeed selects examples that are informative. Another recent system that uses selective experience is ID5R (Utgoff, 1989). The ID5R tree induction algorithm is an incremental version of ID3 (Quinlan, 1986) ID5R employs an experience filter which accepts only instances which would be misclassified by the current decision tree. The algorithm produces smaller trees (than those produced by ID3 on the same data set) ....
Utgoff, P. E. (1989). Improved Training Via Incremental Learning. In Proceedings of The Sixth International Workshop on Machine Learning (pp. 362-365). Ithaca, New York: Morgan Kaufmann.
Heterogeneous Uncertainty Sampling for Supervised Learning - Lewis, Catlett (1994) (70 citations) (Correct)
....has not been established. Single classifier approaches have successfully been used in generating arbitrary queries [16] and in sampling from labeled data [8, 25] Uncertainty sampling with a single classifier can also be viewed as a variation on the heuristic of training on misclassified instances [15, 33, 35]. A familiar example of this is windowing, which appeared in Quinlan s first paper on ID3 [26] was questioned in [36] and re examined in Chapter 6 of the C4.5 book [27] As with uncertainty sampling, windowing builds a sequence of classifiers, selecting instances to add to the training set at ....
Paul E. Utgoff. Improved training via incremental learning. In Sixth InternationalWorkshop on Machine Learning, pages 362--365, 1989.
A Sequential Algorithm for Training Text Classifiers - Lewis, Gale (1994) (122 citations) (Correct)
....criterion or can be easily modified to do so. Perhaps the most difficult requirement is that measurements of relative certainty be produced even when the classifier was formed from very few training examples. Uncertainty sampling is similar to the strategy of training on misclassified instances [16, 17]. The difference is that when data is not labeled we must use the classifier itself to guess at which examples are being misclassified. Note that the initial classifier plays an important role, since without it there may be a long period of random sampling before examples of a low frequency class ....
P. E. Utgoff. Improved training via incremental learning. In Sixth International Workshop on Machine Learning, pages 362--365, 1989.
Learning Concept Classification Rules Using Genetic Algorithms - De Jong, Spears (1991) (13 citations) (Correct)
....in this world) This means that rules map into 16 bit strings and the length of individual rule sets is a multiple of 16. In addition to studying the behavior of GABIL as a function of increasing complexity, we were also interested in comparing its performance with an existing algorithm. ID5R [Utgoff, 1989], which is a well known incremental concept learning algorithm, was chosen for comparison. ID5R uses decision trees as the description language and always produces a decision tree consistent with the instances seen. We constructed a set of 12 concept learning problems, each consisting of a single ....
Utgoff, Paul E. (1989). Improved Training via Incremental Learning, Proc. of the 6th Int'l Workshop on Machine Learning, 62-65.
Incremental Induction of Decision Trees - Utgoff (1989) (78 citations) Self-citation (Utgoff) (Correct)
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Utgoff, P. E. (1989). Improved training via incremental learning. Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann.
An Improved Algorithm for Incremental Induction of Decision Trees - Utgoff (1994) (23 citations) Self-citation (Utgoff) (Correct)
....Improved Algorithm for Incremental Induction of Decision Trees Paul E. Utgoff Technical Report 94 07 February 7, 1994 (updated April 25, 1994) Department of Computer Science University of Massachusetts Amherst, MA 01003 utgoff cs.umass.edu This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for ....
Utgoff, P. E. (1989a). Improved training via incremental learning. Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann.
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