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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 801–810 of 2050 papers

TitleStatusHype
A simple application of FIC to model selection—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
Forward utilities and Mean-field games under relative performance concerns—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Forward and Backward Feature Selection for Query Performance Prediction—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
A Sentiment Analysis of Medical Text Based on Deep Learning—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Forecasting large collections of time series: feature-based methods—0
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