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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 1551–1560 of 2050 papers

TitleStatusHype
On the Computation and Applications of Large Dense Partial Correlation Networks—0
Regularity Normalization: Constraining Implicit Space with Minimum Description Length—0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
Convex Covariate Clustering for ClassificationCode0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Machine learning in policy evaluation: new tools for causal inferenceCode0
On the complexity of logistic regression models—0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Representation Learning with Weighted Inner Product for Universal Approximation of General SimilaritiesCode0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
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