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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 826–850 of 2050 papers

TitleStatusHype
General Hannan and Quinn Criterion for Common Time Series—0
A new approach in model selection for ordinal target variables—0
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Determination of Latent Dimensionality in International Trade Flow—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
A Bayesian Model for Bivariate Causal Inference—0
Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory—0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier—0
A Statistical Framework for Model Selection in LSTM Networks—0
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
Detection and Evaluation of Clusters within Sequential Data—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
Global Adaptive Generative Adjustment—0
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model—0
Bayesian CART models for insurance claims frequency—0
GPT in Data Science: A Practical Exploration of Model Selection—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
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