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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 1251–1275 of 2050 papers

TitleStatusHype
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!—0
EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Hierarchical Block Structures and High-resolution Model Selection in Large Networks—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality—0
Higher-order asymptotics for the parametric complexity—0
High SNR Consistent Compressive Sensing—0
Homotopy Continuation Approaches for Robust SV Classification and Regression—0
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
How Many Communities Are There?—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
How to select predictive models for causal inference?—0
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
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