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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 1476–1500 of 2050 papers

TitleStatusHype
Model Selection for Production System via Automated Online Experiments—0
Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis—0
Model Selection for Simulator-based Statistical Models: A Kernel Approach—0
Model Selection for Topic Models via Spectral Decomposition—0
Model Selection for Type-Supervised Learning with Application to POS Tagging—0
Model Selection in Batch Policy Optimization—0
Model Selection in Contextual Stochastic Bandit Problems—0
Model Selection in High-Dimensional Misspecified Models—0
Model Selection in High-Dimensional Block-Sparse Linear Regression—0
Model Selection in Reinforcement Learning with General Function Approximations—0
Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing—0
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data—0
Model selection of polynomial kernel regression—0
Model Selection's Disparate Impact in Real-World Deep Learning Applications—0
Model Selection Techniques -- An Overview—0
Model Selection Through Model Sorting—0
Model Selection via MCRB Optimization—0
Model selection with Gini indices under auto-calibration—0
Model Selection With Graphical Neighbour Information—0
Model Selection for Generic Reinforcement Learning—0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation—0
Model-specific Data Subsampling with Influence Functions—0
Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis—0
MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining—0
MOFA: Modular Factorial Design for Hyperparameter Optimization—0
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