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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 1801–1825 of 2050 papers

TitleStatusHype
MSBoost: Using Model Selection with Multiple Base Estimators for Gradient BoostingCode0
Priors for symbolic regressionCode0
Hierarchical clustering: visualization, feature importance and model selectionCode0
MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property RetrievalCode0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Multiclass Learning from ContradictionsCode0
High-dimensional classification by sparse logistic regressionCode0
Multiclass Universum SVMCode0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Probabilistic Boolean Tensor DecompositionCode0
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributionsCode0
Multi-locus data distinguishes between population growth and multiple merger coalescentsCode0
High-Fidelity Transfer of Functional Priors for Wide Bayesian Neural Networks by Learning ActivationsCode0
Multimodal Benchmarking and Recommendation of Text-to-Image Generation ModelsCode0
Distributionally Robust Formulation and Model Selection for the Graphical LassoCode0
SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree ModelsCode0
Probabilistic Matrix Factorization for Automated Machine LearningCode0
Probabilistic Modeling for Sequences of Sets in Continuous-TimeCode0
How False Data Affects Machine Learning Models in Electrochemistry?Code0
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry DataCode0
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Multiple Testing and Variable Selection along the path of the Least Angle RegressionCode0
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