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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 1311–1320 of 2050 papers

TitleStatusHype
Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics—0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
The huge Package for High-dimensional Undirected Graph Estimation in R—0
Statistical inference of assortative community structures—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Model family selection for classification using Neural Decision Trees—0
Open Problem: Model Selection for Contextual Bandits—0
Offline detection of change-points in the mean for stationary graph signalsCode0
Selecting Diverse Models for Scientific Insight—0
Towards an Unsupervised Method for Model Selection in Few-Shot Learning—0
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