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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 1881–1890 of 2050 papers

TitleStatusHype
Bridging AIC and BIC: a new criterion for autoregression—0
Learning Structural Kernels for Natural Language Processing—0
Universal Approximation of Edge Density in Large Graphs—0
Topic Stability over Noisy Sources—0
Robustness in sparse linear models: relative efficiency based on robust approximate message passing—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Homotopy Continuation Approaches for Robust SV Classification and Regression—0
Adaptive Mixtures of Factor AnalyzersCode0
Model Selection for Type-Supervised Learning with Application to POS Tagging—0
On the Equivalence of Factorized Information Criterion Regularization and the Chinese Restaurant Process Prior—0
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