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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 1771–1780 of 2050 papers

TitleStatusHype
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs—0
Decision-Aware Predictive Model Selection for Workforce Allocation—0
Decision Making with Machine Learning and ROC Curves—0
Understanding Short-Term Implied Volatility Dynamics: A Model-Independent Approach Beyond Stochastic Volatility—0
Deep Bayes Factors—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Convolutional Neural Networks for Smile Recognition—0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
Deep Gaussian Processes—0
Deep learning for scene recognition from visual data: a survey—0
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