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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 1651–1660 of 2050 papers

TitleStatusHype
An Approach to the CLPsych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection—0
Bayesian Learning with Wasserstein Barycenters—0
A Local Information Criterion for Dynamical Systems—0
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
Best of many worlds: Robust model selection for online supervised learning—0
Parsimonious Bayesian deep networksCode0
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
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