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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 1481–1490 of 2050 papers

TitleStatusHype
ALMERIA: Boosting pairwise molecular contrasts with scalable methods—0
A Local Information Criterion for Dynamical Systems—0
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
A multi-stage machine learning model on diagnosis of esophageal manometry—0
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