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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 1331–1340 of 2050 papers

TitleStatusHype
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms—0
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits—0
Fuzzy c-Means Clustering for Persistence DiagramsCode1
DGSAC: Density Guided Sampling and Consensus—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
Learning Opinion Dynamics From Social TracesCode1
Unsupervised Discretization by Two-dimensional MDL-based HistogramCode0
Uncertainty Based Camera Model SelectionCode1
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