| Moore, A.W., Lee, M.S.: Cached su#cient statistics for e#cient machine learning with large datasets. Journal of Artificial Intelligence Research 8 (1998) 67--91 |
....time is spent in computing cooccurrences. This prompts future work on learning statistical models over large domains that focus on the e#cient computation and usage of the relevant su#cient statistics. Work in this direction includes the structural EM algorithm [14, 16] as well as the A D trees of [37]. The latter are closely related to our representation of the pairwise marginals P uv by counts. In fact, our representation can be viewed as a reduced A D tree that stores only pairwise statistics. Consequently, when an A D tree representation is already computed, it can be exploited in steps 1 ....
Moore, A. W., and Lee, M. S. Cached su#cient statistics for e#cient machine learning with large datasets. Journal for Artificial Intelligence Research 8 (1998), 67--91.
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A. W. Moore and M. S. Lee. Cached su#cient statistics for e#cient machine learning with large datasets. Journal of Artificial Intelligence Research, 8:67--91, 1998.
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Moore, A.W., Lee, M.S.: Cached su#cient statistics for e#cient machine learning with large datasets. Journal of Artificial Intelligence Research 8 (1998) 67--91
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Moore, A.W., Lee, M.S.: Cached su#cient statistics for e#cient machine learning with large datasets. Journal of Artificial Intelligence Research 8 (1998) 67--91 D. Caragea et al.
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Andrew Moore and Mary Soon Lee. Cached su#cient statistics for e#- cient machine learning with large datasets. Journal of Artificial Intelligence Research, 8:67--91, 1998.
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Moore, A. and Lee, M.S. Cached Su#cient Statistics for E#cient Machine Learning with Large Datasets, JAIR, Volume 8, pages 67-91, 1998.
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Andrew Moore and Mary Soon Lee. Cached su#cient statistics for e#- cient machine learning with large datasets. Journal of Artificial Intelligence Research, 8:67--91, 1998.
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