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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 1851–1875 of 2050 papers

TitleStatusHype
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
On Column Selection in Approximate Kernel Canonical Correlation Analysis—0
Active Learning Algorithms for Graphical Model Selection—0
Deep Learning For Smile Recognition—0
Cox process representation and inference for stochastic reaction-diffusion processes—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models—0
Kauffman's adjacent possible in word order evolution—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Short-time asymptotics for the implied volatility skew under a stochastic volatility model with L\'evy jumps—0
Selective Sequential Model Selection—0
Precision-Recall-Gain Curves: PR Analysis Done RightCode0
Bayesian Active Model Selection with an Application to Automated Audiometry—0
Bayesian Network Models for Adaptive Testing—0
Towards Arbitrary-View Face Alignment by Recommendation Trees—0
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
Sacrificing information for the greater good: how to select photometric bands for optimal accuracyCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
NYTRO: When Subsampling Meets Early StoppingCode0
Causal Falling Rule Lists—0
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnaceCode0
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