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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 841–850 of 2050 papers

TitleStatusHype
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution—0
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets—0
Impact of Loss Model Selection on Power Semiconductor Lifetime Prediction in Electric Vehicles—0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
Pushing the limits of fairness impossibility: Who's the fairest of them all?—0
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity—0
SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection—0
Meta Learning for High-dimensional Ising Model Selection Using _1-regularized Logistic Regression—0
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