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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 1351–1375 of 2050 papers

TitleStatusHype
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
Learning for Multi-Model and Multi-Type Fitting—0
Learning from Domain Complexity—0
Learning from missing data with the Latent Block Model—0
Learning Gaussian Graphical Models via Multiplicative Weights—0
Learning high-dimensional probability distributions using tree tensor networks—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Learning manifold to regularize nonnegative matrix factorization—0
Learning of networked spreading models from noisy and incomplete data—0
Learning Sparse Neural Networks through L_0 Regularization—0
Learning Structural Kernels for Natural Language Processing—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Learning the Hypotheses Space from data Part II: Convergence and Feasibility—0
Learning the Markov order of paths in a network—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning under Singularity: An Information Criterion improving WBIC and sBIC—0
Learning Vine Copula Models For Synthetic Data Generation—0
Learning with many experts: model selection and sparsity—0
Learning with tree tensor networks: complexity estimates and model selection—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models—0
( β, )-stability for cross-validation and the choice of the number of folds—0
Leveraging free energy in pretraining model selection for improved fine-tuning—0
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