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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 1361–1370 of 2050 papers

TitleStatusHype
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence—0
DriveML: An R Package for Driverless Machine LearningCode1
Automatic Catalog of RRLyrae from 14 million VVV Light Curves: How far can we go with traditional machine-learning?Code0
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings—0
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and SuggestionsCode0
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline EvaluationCode1
Boxer: Interactive Comparison of Classifier Results—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
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