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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 281–290 of 2050 papers

TitleStatusHype
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
On the Existence of Simpler Machine Learning Models—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
A Strong Baseline for Batch Imitation Learning—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
Active Learning Algorithms for Graphical Model Selection—0
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection—0
A Statistical Framework for Model Selection in LSTM Networks—0
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