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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 1301–1310 of 2050 papers

TitleStatusHype
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Inferring Network Structure From Data—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Information-based inference for singular models and finite sample sizes: A frequentist information criterion—0
Information criteria for non-normalized models—0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
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