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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 1176–1200 of 2050 papers

TitleStatusHype
Network Estimation by Mixing: Adaptivity and More—0
Robust Model Selection and Nearly-Proper Learning for GMMs—0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Morphological Segmentation for SenecaCode0
Corpus-Based Paraphrase Detection Experiments and Review—0
Graph Similarity Description: How Are These Graphs Similar?—0
Model Selection for Production System via Automated Online Experiments—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Vector autoregression models with skewness and heavy tails—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
Hypothesis Testing for Equality of Latent Positions in Random Graphs—0
Laplace Redux - Effortless Bayesian Deep Learning—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
On-the-fly learning of adaptive strategies with bandit algorithms—0
Towards Model Selection using Learning Curve Cross-ValidationCode0
Achieving Fairness with a Simple Ridge Penalty—0
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming—0
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Model Selection CNN-based VVC QualityEnhancement—0
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion—0
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation—0
Synthetic Data for Model Selection—0
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT RankersCode0
The Future of Employment Revisited: How Model Selection Determines Automation Forecasts—0
SMLSOM: The shrinking maximum likelihood self-organizing mapCode0
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