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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 1581–1590 of 2050 papers

TitleStatusHype
Automated Model Selection with Bayesian Quadrature—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Automatic Model Selection for Neural Networks—0
Automatic Relevance Determination For Deep Generative Models—0
Automatic Response Category Combination in Multinomial Logistic Regression—0
Automatic Selection of t-SNE Perplexity—0
Automatic Unsupervised Outlier Model Selection—0
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