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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 1631–1640 of 2050 papers

TitleStatusHype
Bayesian Variable Selection for Globally Sparse Probabilistic PCA—0
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Belief propagation for permutations, rankings, and partial orders—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
Benchmarking the rationality of AI decision making using the transitivity axiom—0
Best of Both Worlds Model Selection—0
Best of many worlds: Robust model selection for online supervised learning—0
Best Practices for Text Annotation with Large Language Models—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
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