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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 1276–1300 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
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic—0
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
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary OrderCode0
Additive interaction modelling using I-priorsCode0
The Minimum Description Length Principle for Pattern Mining: A Survey—0
Tighter risk certificates for neural networksCode1
Prediction in latent factor regression: Adaptive PCR and beyond—0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
Extended Stochastic Block Models with Application to Criminal NetworksCode1
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