|
7
|
Statistical significance in inductive learning
– Olivier Gascuel, Gilles Caraux
- 1992
|
|
55
|
On the Connection Between In-sample Testing and Generalization Error
– D Wolpert
- 1992
|
|
2888
|
Induction of Decision Trees
– J. R. Quinlan
- 1986
|
|
3145
|
R.: Classification and Regression Trees
– L Breiman, J Friedman, C Stone, Olshen
- 1984
|
|
60
|
The Effects of Training Set Size on Decision Tree Complexity
– Oates
- 1997
|
|
16
|
Overfitting Explained
– Paul R. Cohen , David Jensen
- 1997
|
|
13
|
Which method learns most from the data
– A Feelders, W Verkooijen
- 1995
|
|
3
|
Labeling space: A tool for thinking about significance testing in knowledge discovery. Office of Technology Assessment
– David Jensen
- 1995
|
|
32
|
Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice
– Arthur Flexer
- 1994
|
|
13
|
Data mining as an industry
– F T Denton
- 1985
|
|
5
|
Statistical thinking for behavioral scientists
– D K Hildebrand
- 1986
|
|
7
|
Statistical tests for comparing supervised learning algorithms (Technical Report
– T Dietterich
- 1996
|
|
89
|
Generalizing from Case Studies: A Case Study
– David W. Aha
- 1992
|
|
385
|
Very simple classification rules perform well on most commonly used datasets
– Robert C. Holte
- 1993
|
|
662
|
Repository of machine learning databases
– P Murphy, D Aha
- 1994
|
|
38
|
Multi-interval discretization of continuous valued attributes for classification learning
– U Fayyad, K B Irani
- 1993
|
|
413
|
Parallel Networks that Learn to Pronounce English Text
– Terrence J. Sejnowski, Charles R. Rosenberg
- 1987
|
|
92
|
Experimental Designs
– W G Cochran, G M Cox
- 1957
|
|
47
|
A Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice
– Lutz Prechelt, Fakultat Fur Informatik
- 1994
|
