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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 1721–1730 of 2050 papers

TitleStatusHype
Competing Models—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Compressive Recovery of Signals Defined on Perturbed Graphs—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
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