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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 1721–1730 of 2050 papers

TitleStatusHype
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Evasion Attacks against Machine Learning at Test Time—0
Network Model Selection for Task-Focused Attributed Network Inference—0
Fixed effects testing in high-dimensional linear mixed models—0
When Is the First Spurious Variable Selected by Sequential Regression Procedures?—0
Automatic Selection of t-SNE Perplexity—0
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications—0
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Data-driven Advice for Applying Machine Learning to Bioinformatics ProblemsCode0
A network approach to topic modelsCode1
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