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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 1001–1010 of 2050 papers

TitleStatusHype
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection—0
Automatic Unsupervised Outlier Model Selection—0
Hierarchical clustering: visualization, feature importance and model selectionCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Pessimistic Model Selection for Offline Deep Reinforcement Learning—0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Learning Conditional Invariance through Cycle ConsistencyCode0
A stacked DCNN to predict the RUL of a turbofan engineCode1
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