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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 1326–1350 of 2050 papers

TitleStatusHype
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
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary OrderCode0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Additive interaction modelling using I-priorsCode0
The Minimum Description Length Principle for Pattern Mining: A Survey—0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
Prediction in latent factor regression: Adaptive PCR and beyond—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional DataCode0
Learning the Markov order of paths in a network—0
Deep learning for scene recognition from visual data: a survey—0
Learning with tree tensor networks: complexity estimates and model selection—0
Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics—0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
The huge Package for High-dimensional Undirected Graph Estimation in R—0
Statistical inference of assortative community structures—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
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