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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 1101–1110 of 2050 papers

TitleStatusHype
Rejoinder on: Minimal penalties and the slope heuristics: a survey—0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks—0
Parameter-wise co-clustering for high-dimensional data—0
Reliable ABC model choice via random forests—0
Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line—0
Response-based Learning for Machine Translation of Open-domain Database Queries—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
Understanding Model Selection For Learning In Strategic Environments—0
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