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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 1351–1360 of 2050 papers

TitleStatusHype
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
Learning for Multi-Model and Multi-Type Fitting—0
Learning from Domain Complexity—0
Learning from missing data with the Latent Block Model—0
Learning Gaussian Graphical Models via Multiplicative Weights—0
Learning high-dimensional probability distributions using tree tensor networks—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Learning manifold to regularize nonnegative matrix factorization—0
Learning of networked spreading models from noisy and incomplete data—0
Learning Sparse Neural Networks through L_0 Regularization—0
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