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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 1481–1490 of 2050 papers

TitleStatusHype
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models—0
Learning Neural Representations for Network Anomaly DetectionCode0
Adaptive spline fitting with particle swarm optimizationCode0
Doubly robust off-policy evaluation with shrinkage—0
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models—0
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Improving classification performance by feature space transformations and model selection—0
Sparsely Activated NetworksCode0
On the Evaluation of Conditional GANsCode0
Change point detection for graphical models in the presence of missing valuesCode0
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