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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 1371–1380 of 2050 papers

TitleStatusHype
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction—0
Variational approach for learning Markov processes from time series data—0
Variational Bayes for high-dimensional linear regression with sparse priors—0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
Variational Inference and Model Selection with Generalized Evidence Bounds—0
Variational Selection of Features for Molecular Kinetics—0
Vector autoregression models with skewness and heavy tails—0
Verifying Learning-Based Robotic Navigation Systems—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
View-graph Selection Framework for SfM—0
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