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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 1501–1510 of 2050 papers

TitleStatusHype
An energy-based comparative analysis of common approaches to text classification in the Legal domain—0
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading—0
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems—0
A new approach in model selection for ordinal target variables—0
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition—0
An Homotopy Algorithm for the Lasso with Online Observations—0
An information criterion for auxiliary variable selection in incomplete data analysis—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
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