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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 1781–1790 of 2050 papers

TitleStatusHype
Modeling non-stationarities in high-frequency financial time series—0
Objective Bayesian Analysis for Change Point Problems—0
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry DataCode0
metboost: Exploratory regression analysis with hierarchically clustered dataCode0
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models—0
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution—0
Parameter Selection Algorithm For Continuous Variables—0
On the Sample Complexity of Graphical Model Selection for Non-Stationary ProcessesCode0
Online Learning with Regularized Kernel for One-class Classification—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
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