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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 201–210 of 2050 papers

TitleStatusHype
Rethinking the Evaluation Protocol of Domain GeneralizationCode1
Rich Feature Construction for the Optimization-Generalization DilemmaCode1
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
On Pitfalls of Test-Time AdaptationCode1
Assumption-lean inference for generalised linear model parametersCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Self-Compatibility: Evaluating Causal Discovery without Ground TruthCode1
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly DetectionCode1
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