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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 981–990 of 2050 papers

TitleStatusHype
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Cox process representation and inference for stochastic reaction-diffusion processes—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Model Selection for Inverse Reinforcement Learning via Structural Risk Minimization—0
CRIX an index for cryptocurrencies—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Is F_1 Score Suboptimal for Cybersecurity Models? Introducing C_score, a Cost-Aware Alternative for Model Assessment—0
Detecting seasonal episodic-like spatiotemporal memory patterns using animal movement modelling—0
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