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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

Showing 19261950 of 2050 papers

TitleStatusHype
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models0
Convex Techniques for Model Selection0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation0
Clustering evolving data using kernel-based methods0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets0
Greedy metrics in orthogonal greedy learning0
Sparse Modeling for Image and Vision Processing0
Bayesian Evidence and Model Selection0
Median Selection Subset Aggregation for Parallel Inference0
Model Selection for Topic Models via Spectral Decomposition0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data0
Graphical LASSO Based Model Selection for Time Series0
Learning manifold to regularize nonnegative matrix factorization0
Unsupervised learning of regression mixture models with unknown number of components0
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection0
"Look Ma, No Hands!" A Parameter-Free Topic Model0
Kernel-based Information Criterion0
Robust Graphical Modeling with t-Distributions0
Volumes of logistic regression models with applications to model selection0
Sparse Partially Linear Additive ModelsCode0
Nonparametric Hierarchical Clustering of Functional Data0
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs0
Techniques for clustering interaction data as a collection of graphs0
Reliable ABC model choice via random forests0
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning0
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