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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 1031–1040 of 2050 papers

TitleStatusHype
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Bayesian Spatial Predictive Synthesis—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
A study on the distribution of social biases in self-supervised learning visual models—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
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