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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 1111–1120 of 2050 papers

TitleStatusHype
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—0
Finding Materialized Models for Model ReuseCode0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in BuildingsCode0
Post-hoc Models for Performance Estimation of Machine Learning Inference—0
Variance function estimation in regression model via aggregation procedures—0
Belief propagation for permutations, rankings, and partial orders—0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
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