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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 1801–1850 of 2050 papers

TitleStatusHype
Robust and Parallel Bayesian Model Selection—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Communication-efficient Distributed Sparse Linear Discriminant Analysis—0
Nonparametric Bayesian inference of the microcanonical stochastic block model—0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases—0
Universum Learning for Multiclass SVM—0
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background—0
Learning conditional independence structure for high-dimensional uncorrelated vector processes—0
GTApprox: surrogate modeling for industrial designCode0
Feedback-Controlled Sequential Lasso Screening—0
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
Iterative Hard Thresholding for Model Selection in Genome-Wide Association StudiesCode0
UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task—0
Using Kernel Methods and Model Selection for Prediction of Preterm Birth—0
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data—0
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Ordering as privileged information—0
Making Tree Ensembles Interpretable: A Bayesian Model Selection ApproachCode0
A review of Gaussian Markov models for conditional independence—0
Interpretability in Linear Brain Decoding—0
Model-Agnostic Interpretability of Machine Learning—0
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)—0
Simultaneous Clustering and Model Selection for Tensor Affinities—0
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso—0
Predictive Coarse-Graining—0
Bayesian Model Selection of Stochastic Block Models—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Bayesian Variable Selection for Globally Sparse Probabilistic PCA—0
The Quality of the Covariance Selection Through Detection Problem and AUC Bounds—0
Combinatorially Generated Piecewise Activation Functions—0
The topology of large Open Connectome networks for the human brain—0
Fast rates with high probability in exp-concave statistical learning—0
Sampling Requirements for Stable Autoregressive Estimation—0
Efficient Distributed Estimation of Inverse Covariance Matrices—0
Markov models for ocular fixation locations in the presence and absence of colour—0
Phase Transitions and a Model Order Selection Criterion for Spectral Graph ClusteringCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators—0
Face Recognition Using Deep Multi-Pose Representations—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
Sparse model selection in the highly under-sampled regime—0
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks—0
Fast model selection by limiting SVM training times—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
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