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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 841–850 of 2050 papers

TitleStatusHype
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
Global Adaptive Generative Adjustment—0
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
GPT in Data Science: A Practical Exploration of Model Selection—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
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