Overfitting

Known as: Underfitting, Over-fitted, Overfit 
In statistics and machine learning, one of the most common tasks is to fit a "model" to a set of training data, so as to be able to make reliable… (More)
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Papers overview

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Highly Cited
2016
Highly Cited
2016
One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and… (More)
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Highly Cited
2014
Highly Cited
2014
Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious… (More)
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Highly Cited
2003
Highly Cited
2003
This paper addresses a common methodological flaw in the comp arison of variable selection methods. A practical approach to guide… (More)
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Highly Cited
2000
Highly Cited
2000
The conventional wisdom is that backprop nets with excess hi dden units generalize poorly. We show that nets with excess capacity… (More)
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Highly Cited
2000
Highly Cited
2000
Although Bayesian model averaging is theoretically the optimal method for combining learned models, it has seen very little use… (More)
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Highly Cited
1997
Highly Cited
1997
Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been… (More)
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Highly Cited
1995
Highly Cited
1995
A central problem in machine learning is supervised learning—that is, learning from labeled training data. For example, a… (More)
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Highly Cited
1995
Highly Cited
1995
The application of feed forward back propagation artificial neural networks with one hidden layer (ANN) to perform the equivalent… (More)
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Highly Cited
1995
Highly Cited
1995
We study the characteristics of learning with ensembles. Solving exactly the simple model of an ensemble of linear students, we… (More)
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Highly Cited
1993
Highly Cited
1993
Strategies for increasing predictive accuracy through selective pruning have been widely adopted by researchers in decision tree… (More)
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