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Abstract: We introduce a new technique for combining multiple learned models. This technique results in a single comprehensible model. This is to be contrasted with current methods that typically combine models by voting. The core of the technique, the DAGGER (Disjoint Aggregation using Example Reduction) algorithm selects examples which provide evidence for each decision region within each local model. A single model is then learned from the union of these selected examples. We describe experiments on... (Update)
Context of citations to this paper: More
...builds on Meta Learning and in addition uses an independent data set in order to discover a comprehensible model. Davies and Edwards [3] follow a di#erent approach to discover a single comprehensible model out of the distributed information sources. The main idea in the...
...environment. In addition, as it is based on Meta Learning, it suffers from the same problems in scaling up. Davies and Edwards [3] follow a different approach to discover a single comprehensible model out of the distributed information sources. The main idea in the...
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BibTeX entry: (Update)
Davies, W., Edwards, P.: DAGGER: A New Approach to Combining Multiple Models Learned from Disjoint Subsets. Machine Learning 2000. http://citeseer.ist.psu.edu/davies00dagger.html More
@misc{ davies00dagger,
author = "W. Davies and P. Edwards",
title = "DAGGER: A New Approach to Combining Multiple Models Learned from Disjoint
Subsets",
text = "Davies, W., Edwards, P.: DAGGER: A New Approach to Combining Multiple Models
Learned from Disjoint Subsets. Machine Learning 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/davies00dagger.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
657 Bagging Predictors - Breiman - 1996
273 The Strength of Weak Learnability - Schapire - 1990
155 An Empirical Comparison of Voting Classification Algorithms:.. - Bauer, Kohavi - 1998
111 Active Learning with Statistical Models - Cohn, Ghahramani et al. - 1996
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71 A Comparative Evaluation of Voting and MetaLearning on Parti.. - Chan, Stolfo - 1995
69 UCI Repository of Machine Learning Databases [http://www (context) - Merz, Murphy - 1998
47 Megainduction: A test flight (context) - Catlett - 1991
35 A Survey of Methods for Scaling Up Inductive Algorithms - Provost, Kolluri - 1999
30 Knowledge Acquisition from Examples Via Multiple Models - Domingos - 1997
24 New Measurements Highlight the Importance of Redundant Knowl.. (context) - Gams - 1989
18 Distributed Machine Learning: Scaling up with Coarse Grained.. (context) - Provost, Hennessy - 1995
15 Knowledge Acquisition via Knowledge Integration - Brazdil, Torgo - 1990
10 Knowledge Discovery Via Multiple Models - Domingos - 1998
8 Why Does Bagging Work (context) - Domingos - 1997
5 Model Combination in the Multiple-Data-Batches Scenario - Ting, Low - 1997
4 Learning in Distributed Artificial Intelligence Systems (context) - Sian - 1991
2 set cover and complementary graph coloring (context) - Halldrsson, k- - 1996
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://logic.stanford.edu/~wdavies/Papers/index.html): More
DAGGER: Using Instance Selection to Combine Multiple Models.. - Davies, Edwards (Correct)
Ph.D. Thesis Proposal: The Communication of Inductive Inferences - Davies (Correct)
Distributed Learning: An Agent-Based Approach to Data-Mining - Davies, Edwards (1995) (Correct)
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