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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 1426–1450 of 2050 papers

TitleStatusHype
A Case for Dataset Specific Profiling—0
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows—0
Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection—0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
Achieving Fairness with a Simple Ridge Penalty—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
A coupled-mechanisms modelling framework for neurodegeneration—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
Action-State Dependent Dynamic Model Selection—0
Active Comparison of Prediction Models—0
Active Learning Algorithms for Graphical Model Selection—0
Active Learning for Undirected Graphical Model Selection—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
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