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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

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Review
2020
Review
2020
Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by… Expand
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Review
2019
Review
2019
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are… Expand
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Review
2019
Review
2019
Abstract Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work… Expand
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Review
2019
Review
2019
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including… Expand
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Review
2017
Review
2017
In this paper, we focus on how to create data-to-text corpora which can support the learning of wide-coverage micro-planners i.e… Expand
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Review
2017
Review
2017
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various… Expand
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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… Expand
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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
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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
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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… Expand
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