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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 861–870 of 2050 papers

TitleStatusHype
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Correcting Model Bias with Sparse Implicit Processes—0
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!—0
Cost-Effective Online Contextual Model Selection—0
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier SystemCode0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning—0
Model Selection in Reinforcement Learning with General Function Approximations—0
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