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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 1826–1850 of 2050 papers

TitleStatusHype
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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