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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 1351–1360 of 2050 papers

TitleStatusHype
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
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining—0
Regret Balancing for Bandit and RL Model Selection—0
Speedy Performance Estimation for Neural Architecture SearchCode0
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