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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 1341–1350 of 2050 papers

TitleStatusHype
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional DataCode0
Learning the Markov order of paths in a network—0
Deep learning for scene recognition from visual data: a survey—0
Learning with tree tensor networks: complexity estimates and model selection—0
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
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