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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 1626–1650 of 2050 papers

TitleStatusHype
Simultaneous Localization and Layout Model Selection in Manhattan Worlds—0
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion—0
View-graph Selection Framework for SfM—0
Data-driven discovery of PDEs in complex datasetsCode0
A Survey on Theoretical Advances of Community Detection in Networks—0
Parameter-wise co-clustering for high-dimensional data—0
Multiclass Universum SVMCode0
On an improvement of LASSO by scaling—0
Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale—0
Use Of Vapnik-Chervonenkis Dimension in Model Selection—0
Robust high dimensional factor models with applications to statistical machine learning—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation—0
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
Cross Validation Based Model Selection via Generalized Method of Moments—0
Is it worth it? Budget-related evaluation metrics for model selection—0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Optimal design of experiments to identify latent behavioral typesCode0
Automatic Gradient BoostingCode0
Pairwise Covariates-adjusted Block Model for Community Detection—0
Algebraic Equivalence of Linear Structural Equation ModelsCode0
Probabilistic Boolean Tensor DecompositionCode0
Variational Inference and Model Selection with Generalized Evidence Bounds—0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP ModelsCode0
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