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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 876–900 of 2050 papers

TitleStatusHype
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—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
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—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
A Survey on Theoretical Advances of Community Detection in 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
Comparing Bayesian Models of Annotation—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
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Higher-order asymptotics for the parametric complexity—0
Detection and Evaluation of Clusters within Sequential Data—0
High SNR Consistent Compressive Sensing—0
Bayesian CART models for insurance claims frequency—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—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
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
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