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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 1851–1900 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
Higher-order asymptotics for the parametric complexity—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Deep Online Convex Optimization by Putting Forecaster to Sleep—0
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis—0
Deep Convolutional Neural Networks for Smile Recognition—0
Adaptive Online Learning—0
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks—0
Bridging AIC and BIC: a new criterion for autoregression—0
Learning Structural Kernels for Natural Language Processing—0
Universal Approximation of Edge Density in Large Graphs—0
Topic Stability over Noisy Sources—0
Robustness in sparse linear models: relative efficiency based on robust approximate message passing—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Homotopy Continuation Approaches for Robust SV Classification and Regression—0
Adaptive Mixtures of Factor AnalyzersCode0
Model Selection for Type-Supervised Learning with Application to POS Tagging—0
On the Equivalence of Factorized Information Criterion Regularization and the Chinese Restaurant Process Prior—0
Selective Inference and Learning Mixed Graphical Models—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Information-based inference for singular models and finite sample sizes: A frequentist information criterion—0
A simple application of FIC to model selection—0
Generalized Additive Model Selection—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
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