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Model selection

Known as: Model comparison 
Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of… 
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Related topics

Related topics

39 relations
Additive modelAkaike information criterionAlgorithmic information theoryAll models are wrong

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP. The library provides… 
2014
2014
Dynamics and cosmological constraints on Brans-Dicke cosmology
We investigate observational constraints on the Brans-Dicke cosmological model using observational data coming from distant… 
2014
2014
Minimum divergence estimation of speaker prior in multi-session PLDA scoring
Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling speaker and channel variability in the i… 
2010
2010
Satellite remote sensing of Asian aerosols : a case study of clean , polluted and dust storm days
Introduction Conclusions References 
2008
2008
On the Reliability of Clustering Stability in the Large Sample Regime
where θ, θ′ ∈ Θ are the solutions returned by Ak(S1), Ak(S2), and S1, S2 are random samples, each of size m, drawn i.i.d from the… 
2006
2006
Uniqueness of Non-Gaussian Subspace Analysis
Dimension reduction provides an important tool for preprocessing large scale data sets. A possible model for dimension reduction… 
2001
2001
Feature Space Restructuring for SVMs with Application to Text Categorization
In this paper, we propose a new method of text categorization based on feature space restructuring for SVMs. In our method… 
1998
1998
WHY WE NEED AN R 2 MEASURE OF FIT (AND NOT ONLY ONE) IN PROC LOGISTIC AND PROC GENMOD
We propose to use two seemingly different R 2 measures of fit in PROC LOGISTIC and PROC GENMOD (SAS/STAT), and we show that they… 
1998
1998
Incremental Learning With Sample Queries
The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional… 
Highly Cited
1995
Highly Cited
1995
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions… 
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