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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 1681–1690 of 2050 papers

TitleStatusHype
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Train on Validation: Squeezing the Data Lemon—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Region Detection in Markov Random Fields: Gaussian Case—0
Neural Architecture Search with Bayesian Optimisation and Optimal TransportCode0
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility ReportCode0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
A Work Zone Simulation Model for Travel Time Prediction in a Connected Vehicle Environment—0
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