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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 721–730 of 2050 papers

TitleStatusHype
Compressive Recovery of Signals Defined on Perturbed Graphs—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Local Information Criterion for Dynamical Systems—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
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