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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 1161–1170 of 2050 papers

TitleStatusHype
Selective Inference and Learning Mixed Graphical Models—0
Selective Inference for Latent Block Models—0
Selective linear segmentation for detecting relevant parameter changes—0
Selective Sequential Model Selection—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Self-directed Machine Learning—0
Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models—0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection—0
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