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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 1151–1200 of 2050 papers

TitleStatusHype
Expert Finding in Community Question Answering: A Review—0
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries—0
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations—0
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting—0
Explainable Multi-class Classification of Medical Data—0
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models—0
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae—0
Exploring Dynamic Novel View Synthesis Technologies for Cinematography—0
Exploring linguistic feature and model combination for speech recognition based automatic AD detection—0
Exploring the Potential of SSL Models for Sound Event Detection—0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
Face Recognition Using Deep Multi-Pose Representations—0
Face Recognition using Optimal Representation Ensemble—0
Factor-Augmented Regularized Model for Hazard Regression—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Factors in Fashion: Factor Analysis towards the Mode—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Fast and fully-automated histograms for large-scale data sets—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Fast Linear Model Trees by PILOT—0
Fast model selection by limiting SVM training times—0
Fast rates with high probability in exp-concave statistical learning—0
Fast sampling and model selection for Bayesian mixture models—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
Feedback-Controlled Sequential Lasso Screening—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
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Fixed effects testing in high-dimensional linear mixed models—0
Forecasting large collections of time series: feature-based methods—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Forward and Backward Feature Selection for Query Performance Prediction—0
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