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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 1731–1740 of 2050 papers

TitleStatusHype
When Is the First Spurious Variable Selected by Sequential Regression Procedures?—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
Nonparametric weighted stochastic block models—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Sparse model selection via integral terms—0
LIMSI@CoNLL'17: UD Shared Task—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
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