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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 1391–1400 of 2050 papers

TitleStatusHype
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and SuggestionsCode0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots—0
Boxer: Interactive Comparison of Classifier Results—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
Reliable Time Prediction in the Markov Stochastic Block ModelCode0
Inferring Network Structure From Data—0
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage—0
Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough PatentsCode0
Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training—0
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