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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 1191–1200 of 2050 papers

TitleStatusHype
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Fixed effects testing in high-dimensional linear mixed models—0
Forecasting large collections of time series: feature-based methods—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Forward and Backward Feature Selection for Query Performance Prediction—0
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