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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 1251–1260 of 2050 papers

TitleStatusHype
Model-specific Data Subsampling with Influence Functions—0
Online Active Model Selection for Pre-trained ClassifiersCode1
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Model Selection for Cross-Lingual TransferCode0
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images—0
On the Role of Supervision in Unsupervised Constituency Parsing—0
Short-term prediction of photovoltaic power generation using Gaussian process regression—0
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search—0
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function—0
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