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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 1511–1520 of 2050 papers

TitleStatusHype
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithmsCode0
Deep Active Learning with Adaptive AcquisitionCode0
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Model selection for high-dimensional linear regression with dependent observations—0
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization—0
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
Using anomaly detection to support classification of fast running (packaging) processes—0
Estimating Real Log Canonical Thresholds—0
Off-Policy Evaluation via Off-Policy Classification—0
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