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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 2001–2025 of 2050 papers

TitleStatusHype
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars—0
Mixture Model Averaging for Clustering—0
Active Comparison of Prediction Models—0
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Weighted Likelihood Policy Search with Model Selection—0
Deep Gaussian Processes—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Online Learning with Predictable Sequences—0
Model Selection for Degree-corrected Block Models—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Fast Cross-Validation via Sequential TestingCode0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
Scikit-learn: Machine Learning in PythonCode0
Large Scale Correlation Clustering OptimizationCode0
Sparse Estimation with Structured Dictionaries—0
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers—0
Greedy Model Averaging—0
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