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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 1281–1290 of 2050 papers

TitleStatusHype
Modelling brain lesion volume in patches with CNN-based Poisson Regression—0
Finding the Homology of Decision Boundaries with Active LearningCode0
Online Model Selection for Reinforcement Learning with Function Approximation—0
MOFA: Modular Factorial Design for Hyperparameter Optimization—0
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodelCode0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Nonparametric Variable Screening with Optimal Decision Stumps—0
Evaluating Word Embeddings on Low-Resource Languages—0
Estimation vs Metrics: is QE Useful for MT Model Selection?—0
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks—0
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