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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 1876–1900 of 2050 papers

TitleStatusHype
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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