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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 1181–1190 of 2050 papers

TitleStatusHype
Fast model selection by limiting SVM training times—0
Fast rates with high probability in exp-concave statistical learning—0
Fast sampling and model selection for Bayesian mixture models—0
Feature-based model selection for object detection from point cloud data—0
Feature Selection Methods for Cost-Constrained Classification in Random Forests—0
Improving classification performance by feature space transformations and model selection—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
Federated Model Search via Reinforcement Learning—0
Feedback-Controlled Sequential Lasso Screening—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
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