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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 1271–1280 of 2050 papers

TitleStatusHype
Conjugate Mixture Models for Clustering Multimodal Data—0
Efficient model selection in switching linear dynamic systems by graph clustering—0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Online Model Selection: a Rested Bandit Formulation—0
Machine learning with incomplete datasets using multi-objective optimization models—0
The temporal overfitting problem with applications in wind power curve modelingCode0
Proximity Operator of the Matrix Perspective Function and its Applications—0
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data—0
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre studyCode0
Physics-Informed Neural State Space Models via Learning and Evolution—0
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