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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 1711–1720 of 2050 papers

TitleStatusHype
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Nonparametric Independence Screening via Favored Smoothing Bandwidth—0
Parameter Reference Loss for Unsupervised Domain Adaptation—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Network Model Selection Using Task-Focused Minimum Description Length—0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Nonsparse learning with latent variables—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
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