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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 971–980 of 2050 papers

TitleStatusHype
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Loss-guided Stability Selection—0
Dependence model assessment and selection with DecoupleNets—0
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Discovering Distribution Shifts using Latent Space RepresentationsCode0
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
Learning Curves for Decision Making in Supervised Machine Learning: A Survey—0
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