|
94
|
Wrappers For Performance Enhancement And Oblivious Decision Graphs
– Ron Kohavi, Yoav Shoham, Jerry Friedman
- 1995
|
|
14
|
Improved Class Probability Estimates from Decision Tree Models
– Dragos D. Margineantu, Thomas G. Dietterich
- 2001
|
|
|
k. Results indicate that this procedure is very effective in estimating good feature weights (Table 4.8). Particularly the results obtained in the
– Banded Sinusoidal Tasks
- 1994
|
|
102
|
Machine-Learning Research -- Four Current Directions
– Thomas G. Dietterich
|
|
29
|
Nearest neighbor classification from multiple feature subsets
– Stephen D. Bay
- 1999
|
|
122
|
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
– Sreerama K. Murthy
- 1997
|
|
449
|
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
– Eric Bauer, Ron Kohavi
- 1999
|
|
775
|
Wrappers for feature subset selection
– Ron Kohavi , George H. John
- 1997
|
|
115
|
Inference for the generalization error
– Série Scientifique, École Des Hautes Études Commerciales, École Polytechnique, Université Concordia, Université De Montréal, Université Laval, Université Mcgill, Bell Québec, Claude Nadeau, Claude Nadeau, Yoshua Bengio, Yoshua Bengio
- 2003
|
|
27
|
Classification and Regression using Mixtures of Experts
– Steven Richard Waterhouse
- 1997
|
|
11
|
A comprehensive case study: An examination of machine learning and connectionist algorithms
– Frederick Zarndt
- 1995
|
|
364
|
An experimental comparison of three methods for constructing ensembles of decision trees
– Thomas G. Dietterich, Doug Fisher
- 2000
|
|
131
|
Error-Correcting Output Coding Corrects Bias and Variance
– Eun Bae Kong, Thomas G. Dietterich
- 1995
|
|
39
|
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
– Stephen D. Bay
|
|
2
|
Effective Pruning of Neural Network Classifier Ensembles
– Aleksandar Lazarevic, Ar Lazarevic, Zoran Obradovic
|
|
22
|
Prototype Selection for Composite Nearest Neighbor Classifiers
– David B. Skalak
- 1997
|
|
151
|
Popular ensemble methods: an empirical study
– David Opitz, Richard Maclin
- 1999
|
|
144
|
Bias plus variance decomposition for zero-one loss functions
– Ron Kohavi
- 1996
|
|
50
|
Tree induction vs. logistic regression: A learning-curve analysis
– Claudia Perlich, Foster Provost, Jeffrey S. Simonoff
- 2001
|