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Under review
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Learning the structure of linear latent
variable models . With Richard
Scheines, Clark Glymour and Peter Spirtes. A formal approach for
identifying
structural features in linear graphs where no conditional independencies are
observable.
To the best of our knowledge, this the first work to state
theoretical guarantees of
identifiability;
Selected publications
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Silva, R. and Scheines, R. (2005). New
d-separation identification results for learning continuous latent variable
models. Proceedings of the International Conference in Machine Learning,
ICML 05. Tech report version.
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Silva, R.; Zhang, J. and Shanahan, J. G. (2005). Probabilistic workflow mining.
Proceedings of Knowledge Discovery and Data Mining, KDD 05. Tech
report version.
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Silva, R.; Scheines, R.; Glymour, C. and Spirtes P. (2003) "Learning
measurement models for unobserved variables". Proceedings
of the 19th Conference on Uncertainty on Artificial Intelligence.
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Moody, J.; Silva, R.; Vanderwaart, J. and Glymour, C. (2001). "Data
filtering for automatic classification of rocks from reflectance spectra".
Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining, p. 347-352. ACM Press, San Francisco, CA.
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Silva, R. B. A. and Ludermir, T. B. (2001). “Hybrid
systems of local basis functions”. Intelligent Data Analysis 5
(3), 227-244
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Silva, R. B. A. and Ludermir, T. B. (2000). “Obtaining
simplified rules by hybrid learning”. Proceedings of the 17th International
Conference on Machine Learning, 879-886. Morgan Kaufmann, San Francisco,
CA
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