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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 1181–1190 of 2050 papers

TitleStatusHype
Sig-SDEs model for quantitative finance—0
Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study—0
Simultaneous Clustering and Model Selection for Tensor Affinities—0
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models—0
Simultaneous Localization and Layout Model Selection in Manhattan Worlds—0
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning—0
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC—0
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship—0
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Small Data, Big Decisions: Model Selection in the Small-Data Regime—0
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