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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
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
Detection and Evaluation of Clusters within Sequential Data—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
Global Adaptive Generative Adjustment—0
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model—0
Bayesian CART models for insurance claims frequency—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
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
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