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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 701–710 of 2050 papers

TitleStatusHype
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimizationCode0
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption—0
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model AgentsCode0
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging For Cross-Lingual TransferCode0
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data—0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation—0
Target Variable Engineering—0
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