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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 1531–1540 of 2050 papers

TitleStatusHype
Noise Fit, Estimation Error and a Sharpe Information Criterion—0
NoMoPy: Noise Modeling in Python—0
Non-Bayesian Post-Model-Selection Estimation as Estimation Under Model Misspecification—0
Noncommutative Model Selection and the Data-Driven Estimation of Real Cohomology Groups—0
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows—0
Nonparametric Bayesian inference of the microcanonical stochastic block model—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Learning Representations from Dendrograms—0
Non-parametric generalized linear model—0
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