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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 1961–1970 of 2050 papers

TitleStatusHype
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression—0
Exact Post Model Selection Inference for Marginal Screening—0
Student-t Processes as Alternatives to Gaussian Processes—0
Classification with Sparse Overlapping Groups—0
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models—0
Sequential Model-Based Ensemble Optimization—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Markov Network Structure Learning via Ensemble-of-Forests Models—0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsCode0
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