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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 851–860 of 2050 papers

TitleStatusHype
Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data—0
Adaptive LASSO estimation for functional hidden dynamic geostatistical model—0
Boosting with copula-based components—0
An Optimal Likelihood Free Method for Biological Model Selection—0
A Case for Dataset Specific Profiling—0
Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model—0
Model selection with Gini indices under auto-calibration—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
Robust Output Analysis with Monte-Carlo Methodology—0
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution DiscriminationCode0
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