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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.
2015
2015
The Role of Data Mining for Business Intelligence in Knowledge Management
This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining… 
2013
2013
Combination of activation functions in extreme learning machines for multivariate calibration
2012
2012
Shrink boost for selecting multi-LBP histogram features in object detection
Feature selection from sparse and high dimension features using conventional greedy based boosting gives classifiers of poor… 
2011
2011
Learning Hierarchical Translation Structure with Linguistic Annotations
While it is generally accepted that many translation phenomena are correlated with linguistic structures, employing linguistic… 
2010
2010
Guidelines for developing effective Estimation of Distribution Algorithms in solving single machine scheduling problems
Highly Cited
2010
Highly Cited
2010
Shape measurement biases from underfitting and ellipticity gradients
With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in… 
2003
2003
Simultaneous 1 D inversion of loop – loop electromagnetic data for magnetic susceptibility and electrical conductivity
Magnetic susceptibility affects electromagnetic (EM) loop–loop observations in ways that cannot be replicated by conductive… 
Highly Cited
2000
Highly Cited
2000
Optimal signal multi-resolution by genetic algorithms to support artificial neural networks for exchange-rate forecasting
Highly Cited
1997
Highly Cited
1997
Computing Optimized Rectilinear Regions for Association Rules
We address the problem of finding useful regions for two-dimensional association rules and decision trees. In a previous paper we… 
Highly Cited
1986
Highly Cited
1986
Not the Path to Perdition: The Utility of Similarity-Based Learning
A large portion of the research in machine learning has involved a paradigm of comparing many examples and analyzing them in… 
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