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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 521–530 of 2050 papers

TitleStatusHype
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—0
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets—0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Greedy equivalence search for nonparametric graphical models—0
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic CommunicationCode0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
Exploring Design Choices for Building Language-Specific LLMsCode0
Efficient Sequential Decision Making with Large Language Models—0
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