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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 1951–1975 of 2050 papers

TitleStatusHype
SCAMS: Simultaneous Clustering and Model Selection—0
Learning with many experts: model selection and sparsity—0
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Model Consistency of Partly Smooth Regularizers—0
Learning from Domain Complexity—0
How Many Topics? Stability Analysis for Topic ModelsCode1
Active Learning for Undirected Graphical Model Selection—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Selective Factor Extraction in High Dimensions—0
Optimal interval clustering: Application to Bregman clustering and statistical mixture learning—0
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression—0
Exact Post Model Selection Inference for Marginal Screening—0
Student-t Processes as Alternatives to Gaussian Processes—0
Classification with Sparse Overlapping Groups—0
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models—0
Sequential Model-Based Ensemble Optimization—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Markov Network Structure Learning via Ensemble-of-Forests Models—0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsCode0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Sparse Inverse Covariance Estimation with Calibration—0
Bayesian Hierarchical Community Discovery—0
Learning Adaptive Value of Information for Structured Prediction—0
On model selection consistency of penalized M-estimators: a geometric theory—0
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