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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 1841–1850 of 2050 papers

TitleStatusHype
Markov models for ocular fixation locations in the presence and absence of colour—0
Phase Transitions and a Model Order Selection Criterion for Spectral Graph ClusteringCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators—0
Face Recognition Using Deep Multi-Pose Representations—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
Sparse model selection in the highly under-sampled regime—0
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks—0
Fast model selection by limiting SVM training times—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
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