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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 1321–1330 of 2050 papers

TitleStatusHype
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model SelectionCode0
Volatility Forecasting with 1-dimensional CNNs via transfer learning—0
Visual Sentiment Analysis from Disaster Images in Social Media—0
Non-parametric generalized linear model—0
Automated Model Selection for Time-Series Anomaly Detection—0
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic—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
Batch Value-function Approximation with Only RealizabilityCode0
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