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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 776–800 of 2050 papers

TitleStatusHype
Fast rates with high probability in exp-concave statistical learning—0
Fast sampling and model selection for Bayesian mixture models—0
Cats & Co: Categorical Time Series Coclustering—0
Feature-based model selection for object detection from point cloud data—0
Feature Selection Methods for Cost-Constrained Classification in Random Forests—0
Improving classification performance by feature space transformations and model selection—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
Federated Model Search via Reinforcement Learning—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Feedback-Controlled Sequential Lasso Screening—0
Causal Falling Rule Lists—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Generalized Information Criteria for Structured Sparse Models—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Greedy equivalence search for nonparametric graphical models—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Bayesian Evidence and Model Selection—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
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