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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 1681–1690 of 2050 papers

TitleStatusHype
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI—0
Large-Scale Model Selection with Misspecification—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Piecewise Convex Function Estimation and Model Selection—0
HybridSVD: When Collaborative Information is Not EnoughCode0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Train on Validation: Squeezing the Data Lemon—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Region Detection in Markov Random Fields: Gaussian Case—0
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