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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 541–550 of 2050 papers

TitleStatusHype
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Belief propagation for permutations, rankings, and partial orders—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Disentangling Factors of Variation Using Few Labels—0
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts—0
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Bayesian Variable Selection for Globally Sparse Probabilistic PCA—0
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