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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 1351–1360 of 2050 papers

TitleStatusHype
Systematic Ensemble Model Selection Approach for Educational Data Mining—0
Robust Lasso-Zero for sparse corruption and model selection with missing covariatesCode0
The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data IntegrationCode0
Lossy Compression with Distortion Constrained Optimization—0
Spiking Neural Networks Hardware Implementations and Challenges: a Survey—0
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
BayesOpt Adversarial AttackCode1
Disentangling Factors of Variations Using Few Labels—0
TopicNet: Making Additive Regularisation for Topic Modelling AccessibleCode0
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition—0
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