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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… 
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Related topics

Related topics

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AdaBoostB-splineBackpropagationBacktesting

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2014
Review
2014
Enhancement of Mahalanobis-Taguchi System via Rough Sets based Feature Selection
2013
2013
Empirical analysis of model selection criteria for genetic programming in modeling of time series system
Genetic programming (GP) and its variants have been extensively applied for modeling of the stock markets. To improve the… 
Highly Cited
2011
Highly Cited
2011
A rough margin-based ν-twin support vector machine
Twin support vector machine (TSVM) is a new machine learning algorithm, which aims at finding two nonparallel planes for each… 
2010
2010
Guidelines for developing effective Estimation of Distribution Algorithms in solving single machine scheduling problems
Highly Cited
2008
Highly Cited
2008
Pattern Trees Induction: A New Machine Learning Method
Fuzzy classification is one of the most important applications in fuzzy set and fuzzy-logic-related research. Its goal is to find… 
2007
2007
Calibration of an EMG-Based Body Model with six Muscles to control a Leg Exoskeleton
This paper presents a body model of intermediate level of detail to allow prediction of the knee torque produced by thigh muscles… 
2007
2007
An evaluation of Naive Bayes variants in content-based learning for spam filtering
We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two extended variants. A set… 
Highly Cited
2006
Highly Cited
2006
Constructing response model using ensemble based on feature subset selection
Review
2003
Review
2003
An Introduction to Support Vector Machines: A Review
Review of "An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Nello Cristianini and John Shawe… 
Highly Cited
1998
Highly Cited
1998
Optimizing the number of hidden nodes of a feedforward artificial neural network
The number of hidden nodes is a crucial parameter of a feedforward artificial neural network. A neural network with too many… 
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