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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 1501–1510 of 2050 papers

TitleStatusHype
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming—0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System—0
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction—0
MSM lag time cannot be used for variational model selection—0
Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
Selecting Diverse Models for Scientific Insight—0
Multi-model Stochastic Particle-based Variational Bayesian Inference for Multiband Delay Estimation—0
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