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Generalization error

Known as: Generalisation error, Generalization (disambiguation) 
In supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error… 
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Related topics

Related topics

16 relations
AlgorithmBias–variance tradeoffBrownBoostCross-validation (statistics)

Papers overview

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2015
2015
Joint Semi-supervised Similarity Learning for Linear Classification
The importance of metrics in machine learning has attracted a growing interest for distance and similarity learning. We study… 
2014
2014
A new set of Random Forests with varying dynamic data reduction and voting techniques
Random forests have been used as effective models to tackle a number of classification and regression problems. In this paper, we… 
2013
2013
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss
We propose a modular framework for multi-relational learning via tensor decomposition. In our learning setting, the training data… 
Review
2009
Review
2009
Data Mining: a Magic Technology for College Recruitment
This paper introduces a case study using data mining techniques to assist higher education institutions in achieving enrollment… 
2008
2008
Forecasting Machine Vibration Trends Using Support Vector Machines
Equipment deteriorates as it is used and its vibration level usually increases accordingly. As a result, vibration has been used… 
2007
2007
Face Recognition in Video Using a What-and-Where Fusion Neural Network
A what-and-where fusion neural network is applied to the recognition of human faces from video sequences. The spatio-temporal… 
2006
2006
Video Annotation by Active Learning and Cluster Tuning
Supervised and semi-supervised learning are frequently applied methods to annotate videos by map..ing low-level features into… 
2005
2005
Active learning using localized generalization error of candidate sample as criterion
In classification problem, the learning process can be more efficient if the informative samples can be selected actively based… 
2005
2005
Rotational Prior Knowledge for SVMs
Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show… 
1999
1999
Generalization Error of Linear Neural Networks in Unidentiable Cases
The statistical asymptotic theory is often used in theoreti- cal results in computational and statistical learning theory. It…