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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 1601–1625 of 2050 papers

TitleStatusHype
Physics-Based Learning for Robotic Environmental Sensing—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection—0
Learning Vine Copula Models For Synthetic Data Generation—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
Variational Selection of Features for Molecular Kinetics—0
LM-BIC Model Selection in Semiparametric Models—0
Model Evaluation, Model Selection, and Algorithm Selection in Machine LearningCode0
Model Selection for Generalized Zero-shot Learning—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach—0
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery—0
Model Selection Techniques -- An Overview—0
On The Stability of Interpretable Models—0
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction—0
A Unified Dynamic Approach to Sparse Model Selection—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Cost-Sensitive Learning for Predictive Maintenance—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Lexical Bias In Essay Level Prediction—0
Improving Subseasonal Forecasting in the Western U.S. with Machine LearningCode0
Comparison between Suitable Priors for Additive Bayesian Networks—0
Bayesian Structure Learning by Recursive Bootstrap—0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
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