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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 1701–1710 of 2050 papers

TitleStatusHype
Learning Sparse Neural Networks through L_0 RegularizationCode0
Episodic memory for continual model learning—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Scalable Model Selection for Belief Networks—0
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Nonparametric Independence Screening via Favored Smoothing Bandwidth—0
Population Based Training of Neural NetworksCode1
Parameter Reference Loss for Unsupervised Domain Adaptation—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
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