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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 161–170 of 2050 papers

TitleStatusHype
Task-Agnostic Amortized Inference of Gaussian Process HyperparametersCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Online Active Model Selection for Pre-trained ClassifiersCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Automating Outlier Detection via Meta-LearningCode1
An information criterion for automatic gradient tree boostingCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
Tighter risk certificates for neural networksCode1
Extended Stochastic Block Models with Application to Criminal NetworksCode1
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