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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 1261–1270 of 2050 papers

TitleStatusHype
The Future of Employment Revisited: How Model Selection Determines Automation Forecasts—0
The huge Package for High-dimensional Undirected Graph Estimation in R—0
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers—0
The Infinite Contextual Graph Markov Model—0
The Interpolating Information Criterion for Overparameterized Models—0
The LASSO risk: asymptotic results and real world examples—0
The Majority Vote Paradigm Shift: When Popular Meets Optimal—0
The Minimum Description Length Principle for Pattern Mining: A Survey—0
The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data—0
The Pareto Frontier of model selection for general Contextual Bandits—0
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