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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
A Statistical Framework for Model Selection in LSTM Networks—0
A Junction Tree Framework for Undirected Graphical Model Selection—0
Clustering evolving data using kernel-based methods—0
Clustering Discrete-Valued Time Series—0
Clustering-Based Validation Splits for Model Selection under Domain Shift—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Active Comparison of Prediction Models—0
A Bayesian Perspective on Training Speed and Model Selection—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Clipper: A Low-Latency Online Prediction Serving System—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Classification with Sparse Overlapping Groups—0
On The Stability of Interpretable Models—0
Classification with Scattering Operators—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
Action-State Dependent Dynamic Model Selection—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
A simple application of FIC to model selection—0
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