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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 751–760 of 2050 papers

TitleStatusHype
Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data—0
Communication-efficient Distributed Sparse Linear Discriminant Analysis—0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
A study on the distribution of social biases in self-supervised learning visual models—0
A Latent Gaussian Mixture Model for Clustering Longitudinal Data—0
Active Learning for Undirected Graphical Model Selection—0
Combined l_1 and greedy l_0 penalized least squares for linear model selection—0
Combinatorially Generated Piecewise Activation Functions—0
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