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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 1781–1790 of 2050 papers

TitleStatusHype
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models—0
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution—0
Parameter Selection Algorithm For Continuous Variables—0
Online Learning with Regularized Kernel for One-class Classification—0
On the Sample Complexity of Graphical Model Selection for Non-Stationary ProcessesCode0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Optimal statistical decision for Gaussian graphical model selection—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Network cross-validation by edge sampling—0
Clipper: A Low-Latency Online Prediction Serving System—0
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