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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 1251–1260 of 2050 papers

TitleStatusHype
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion—0
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model—0
The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification—0
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models—0
The column measure and Gradient-Free Gradient Boosting—0
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity—0
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges—0
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks—0
The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model—0
The Evolution of Multimodal Model Architectures—0
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