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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1581–1590 of 2050 papers

TitleStatusHype
On the cross-validation bias due to unsupervised pre-processingCode0
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship—0
lassopack: Model selection and prediction with regularized regression in Stata—0
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—0
Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for TurbineGovernor and Excitation System—0
Automated Adaptation Strategies for Stream LearningCode0
A Bayesian Model for Bivariate Causal Inference—0
Learning Representations from Dendrograms—0
Breaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudes—0
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