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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 691–700 of 2050 papers

TitleStatusHype
Modeling Transient Changes in Circadian Rhythms—0
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges—0
Priors for symbolic regressionCode0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
A principled approach to model validation in domain generalizationCode0
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly DetectionCode1
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging FaceCode6
Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis—0
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
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