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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 1001–1010 of 2050 papers

TitleStatusHype
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models—0
Kernel-based Information Criterion—0
Kernel Spectral Clustering and applications—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Label-Efficient Model Selection for Text Generation—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
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