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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 1571–1580 of 2050 papers

TitleStatusHype
Efficient Cross-Validation for Semi-Supervised Learning—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Model Selection for Simulator-based Statistical Models: A Kernel Approach—0
Un modèle Bayésien de co-clustering de données mixtes—0
Learning Counterfactual Representations for Estimating Individual Dose-Response CurvesCode0
Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models—0
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
Learning for Multi-Model and Multi-Type Fitting—0
Testing Conditional Independence in Supervised Learning AlgorithmsCode1
Clustering Discrete-Valued Time Series—0
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