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by Stella M. Salvatierra
http://www.stat.cmu.edu/~stella/proposal.ps
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Abstract:

models for classification and discrimination with application to classifying web documents

Citations

4344 Maximum likelihood from incomplete data via the EM algorithm – Dempster, Laird, et al. - 1977
1453 Bagging Predictors – Breiman - 1996
716 A K-means clustering algorithm – HARTIGAN, WONG - 1979
575 Combining labeled and unlabeled data with co-training – Blum, Mitchell - 1998
543 Additive logistic regression: a statistical view of boosting – Friedman, Hastie, et al.
483 Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods – Schapire, Freund, et al. - 1997
477 A comparison of event models for Naive Bayes text classification – McCallum, Nigam - 1998
454 Text classification from labeled and unlabeled documents using EM – Nigam, McCallum, et al.
334 On the optimality of the simple bayesian classifier under zero-one loss – Domingos, Pazzani - 1997
207 Discriminant analysis and statistical pattern recognition – McLachlan - 1992
154 Learning to construct knowledge bases from the World Wide Web – Craven, DiPasquo, et al. - 2000
151 On bias, variance, 0/1 - loss, and the curse-of-dimensionality – Friedman - 1997
134 Bias plus variance decomposition for zero-one loss functions – Kohavi, Wolpert - 1996
39 The role of unlabeled data in supervised learning – Mitchell - 1999
28 Cross-validation and the bootstrap: estimating the error rate of a prediction rule – EFRON, TIBSHIRANI - 1995
25 Bias, variance and prediction error for classification rules – Tibshirani - 1996
18 Using adaptive bagging to debias regression – Breiman - 1999
18 A first course in multivariate statistics – Flury - 1997
10 Central Limit Theorems for Multinomial Sums – Morris - 1975
5 Combining classifiers via discretization – Mojirsheibani - 1999
3 Machine learning bias, statistical bias, and statistical variance of decision tree algorithms – Kong - 1995
3 Discrete Discriminant Analysis – Goldstein, Dillon - 1978
2 Learning to extract knowledge from the world wide web – Craven, DiPasquo, et al. - 1998
1 Learning text from the web: An application of classification methods – Salvatierra - 1999