Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

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… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they… Expand
  • figure 1
  • figure 2
  • table 1
  • table 2
  • figure 3
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… Expand
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • table 2
Highly Cited
2014
Highly Cited
2014
Aim Models of species niches and distributions have become invaluable to biogeographers over the past decade, yet several… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
2008
Highly Cited
2008
Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
2003
Highly Cited
2003
This paper addresses a common methodological flaw in the comparison of variable selection methods. A practical approach to guide… Expand
  • table 1
  • figure 1
  • figure 2
  • figure 3
  • table 2
Highly Cited
2000
Highly Cited
2000
The conventional wisdom is that backprop nets with excess hidden units generalize poorly. We show that nets with excess capacity… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
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… Expand
  • figure 2
  • figure 1
  • figure 3
  • figure 4
  • table 1
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… Expand
Highly Cited
1995
Highly Cited
1995
In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction… Expand
  • figure 1
  • figure 3
  • figure 2
  • table 1
  • table 2
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… Expand
  • figure 1
  • figure 2
  • figure 3