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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 1401–1410 of 2050 papers

TitleStatusHype
Marked point processes and intensity ratios for limit order book modeling—0
The Reciprocal Bayesian LASSOCode0
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection—0
Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions—0
Evaluating Weakly Supervised Object Localization Methods RightCode1
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Source Model Selection for Deep Learning in the Time Series DomainCode1
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
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