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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 501–510 of 2050 papers

TitleStatusHype
Bayesian Model Selection via Mean-Field Variational Approximation—0
Random Models for Fuzzy Clustering Similarity Measures—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
Predictive variational autoencoder for learning robust representations of time-series data—0
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey—0
Evaluating the Utility of Model Explanations for Model Development—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
Deep Bayes Factors—0
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework—0
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