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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 911–920 of 2050 papers

TitleStatusHype
Feature Selection Methods for Cost-Constrained Classification in Random Forests—0
Feature-based model selection for object detection from point cloud data—0
Causal Covariate Shift Correction using Fisher information penalty—0
A Review of Change of Variable Formulas for Generative Modeling—0
Fast sampling and model selection for Bayesian mixture models—0
Cats & Co: Categorical Time Series Coclustering—0
Fast rates with high probability in exp-concave statistical learning—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
Fast model selection by limiting SVM training times—0
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