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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 561–570 of 2050 papers

TitleStatusHype
An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Bayesian optimization for automated model selection—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Adaptive variational Bayes: Optimality, computation and applications—0
Gmail Smart Compose: Real-Time Assisted Writing—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
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