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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 1861–1870 of 2050 papers

TitleStatusHype
Bayesian Active Model Selection with an Application to Automated Audiometry—0
Precision-Recall-Gain Curves: PR Analysis Done RightCode0
Bayesian Network Models for Adaptive Testing—0
Towards Arbitrary-View Face Alignment by Recommendation Trees—0
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
Sacrificing information for the greater good: how to select photometric bands for optimal accuracyCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
NYTRO: When Subsampling Meets Early StoppingCode0
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