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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 1901–1950 of 2050 papers

TitleStatusHype
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Automatic Relevance Determination For Deep Generative Models—0
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation—0
Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study—0
Cats & Co: Categorical Time Series Coclustering—0
Kernel Spectral Clustering and applications—0
Response-based Learning for Machine Translation of Open-domain Database Queries—0
Model Selection and Overfitting in Genetic Programming: Empirical Study [Extended Version]—0
Meta learning of bounds on the Bayes classifier error—0
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood—0
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality—0
Comparison of Bayesian predictive methods for model selection—0
Indian Buffet process for model selection in convolved multiple-output Gaussian processesCode0
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
Qualitative inequalities for squared partial correlations of a Gaussian random vector—0
Model selection of polynomial kernel regression—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010—0
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics—0
On model misspecification and KL separation for Gaussian graphical models—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Model Selection in High-Dimensional Misspecified Models—0
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model—0
The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification—0
Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression—0
How Many Communities Are There?—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
Convex Techniques for Model Selection—0
Clustering evolving data using kernel-based methods—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
Greedy metrics in orthogonal greedy learning—0
Sparse Modeling for Image and Vision Processing—0
Bayesian Evidence and Model Selection—0
Median Selection Subset Aggregation for Parallel Inference—0
Model Selection for Topic Models via Spectral Decomposition—0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data—0
Graphical LASSO Based Model Selection for Time Series—0
Learning manifold to regularize nonnegative matrix factorization—0
Unsupervised learning of regression mixture models with unknown number of components—0
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection—0
"Look Ma, No Hands!" A Parameter-Free Topic Model—0
Kernel-based Information Criterion—0
Robust Graphical Modeling with t-Distributions—0
Volumes of logistic regression models with applications to model selection—0
Sparse Partially Linear Additive ModelsCode0
Nonparametric Hierarchical Clustering of Functional Data—0
Techniques for clustering interaction data as a collection of graphs—0
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs—0
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