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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 926–950 of 2050 papers

TitleStatusHype
Identifying Technical Debt and Its Types Across Diverse Software Projects Issues—0
Cats & Co: Categorical Time Series Coclustering—0
Fast rates with high probability in exp-concave statistical learning—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
Fast model selection by limiting SVM training times—0
Fast Linear Model Trees by PILOT—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Improving Group Lasso for high-dimensional categorical data—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Improving Model Robustness Using Causal Knowledge—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
A Regret-Variance Trade-Off in Online Learning—0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
Inconsistency of cross-validation for structure learning in Gaussian graphical models—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education—0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
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