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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 1281–1290 of 2050 papers

TitleStatusHype
Automated Model Selection for Time-Series Anomaly Detection—0
Minimum discrepancy principle strategy for choosing k in k-NN regressionCode0
Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models—0
Feature Selection Methods for Cost-Constrained Classification in Random Forests—0
An information criterion for automatic gradient tree boostingCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
Batch Value-function Approximation with Only RealizabilityCode0
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
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