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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 1601–1610 of 2050 papers

TitleStatusHype
Model Selection for Generalized Zero-shot Learning—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach—0
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery—0
On The Stability of Interpretable Models—0
Model Selection Techniques -- An Overview—0
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction—0
Variational Bayesian Monte CarloCode1
A Unified Dynamic Approach to Sparse Model Selection—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
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