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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 1491–1500 of 2050 papers

TitleStatusHype
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning—0
Multi-view Deep Subspace Clustering NetworksCode0
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces—0
On the Existence of Simpler Machine Learning Models—0
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models—0
Learning Neural Representations for Network Anomaly DetectionCode0
Adaptive spline fitting with particle swarm optimizationCode0
Doubly robust off-policy evaluation with shrinkage—0
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models—0
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