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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.
Highly Cited
2018
Highly Cited
2018
A new image classification method using CNN transfer learning and web data augmentation
Highly Cited
2017
Highly Cited
2017
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Existing systems for video-based pose estimation and tracking struggle to perform well on realistic videos with multiple people… 
Highly Cited
2016
Highly Cited
2016
Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification
While training a model with data from a dataset, we have to think of an ideal way to do so. The training should be done in such a… 
Highly Cited
2015
Highly Cited
2015
Neural Networks with Few Multiplications
For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural… 
Review
2014
Review
2014
Mahalanobis Distance Learning for Person Re-identification
Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea… 
Highly Cited
2004
Highly Cited
2004
Prediction Versus Accommodation and the Risk of Overfitting
When a scientist uses an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that… 
Highly Cited
2003
Highly Cited
2003
Adding Missing-Data-Relevant Variables to FIML-Based Structural Equation Models
Conventional wisdom in missing data research dictates adding variables to the missing data model when those variables are… 
Highly Cited
2003
Highly Cited
2003
Feature selection for support vector machines by means of genetic algorithm
The problem of feature selection is a difficult combinatorial task in machine learning and of high practical relevance, e.g. in… 
Highly Cited
2001
Highly Cited
2001
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
Text categorization presents unique challenges due to the large number of attributes present in the data set, large number of… 
Review
1999
Review
1999
A Guide to Statistical and Data Analysis Using JMP and JMP IN Software
Part I: JMPing IN with both feet: 1. Jump Right In. First Session. Modelling Type. Analyze and Graph. Getting Help: The JMP Help… 
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