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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 411–420 of 2050 papers

TitleStatusHype
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Clipper: A Low-Latency Online Prediction Serving System—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection—0
biastest: Testing parameter equality across different models in Stata—0
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
Clustering evolving data using kernel-based methods—0
A Statistical Framework for Model Selection in LSTM Networks—0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
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