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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 1201–1210 of 2050 papers

TitleStatusHype
Fitting very flexible models: Linear regression with large numbers of parameters—0
Detecting seasonal episodic-like spatiotemporal memory patterns using animal movement modelling—0
General Hannan and Quinn Criterion for Common Time Series—0
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
Explainable Multi-class Classification of Medical Data—0
Upper Confidence Bounds for Combining Stochastic Bandits—0
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