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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 1741–1750 of 2050 papers

TitleStatusHype
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
Graphical posterior predictive classifier: Bayesian model averaging with particle GibbsCode0
Variational approach for learning Markov processes from time series data—0
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix FactorisationCode0
Model Selection for Anomaly Detection—0
CHARDA: Causal Hybrid Automata Recovery via Dynamic AnalysisCode0
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell TimeCode0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
High-dimensional classification by sparse logistic regressionCode0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation—0
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