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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 1301–1310 of 2050 papers

TitleStatusHype
Model Selection for Cross-Lingual TransferCode0
On the Role of Supervision in Unsupervised Constituency Parsing—0
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images—0
Short-term prediction of photovoltaic power generation using Gaussian process regression—0
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
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
Small Data, Big Decisions: Model Selection in the Small-Data Regime—0
A first econometric analysis of the CRIX family—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
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