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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 1661–1670 of 2050 papers

TitleStatusHype
Topological Data Analysis of Decision Boundaries with Application to Model SelectionCode0
Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing—0
Parsimonious Bayesian deep networksCode0
Best of many worlds: Robust model selection for online supervised learning—0
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Analyzing order flows in limit order books with ratios of Cox-type intensities—0
Model selection with lasso-zero: adding straw to the haystack to better find needlesCode0
Spatio-temporal Bayesian On-line Changepoint Detection with Model SelectionCode0
TensOrMachine: Probabilistic Boolean Tensor DecompositionCode0
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