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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 1411–1420 of 2050 papers

TitleStatusHype
Markov Network Structure Learning via Ensemble-of-Forests Models—0
Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones—0
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms—0
Exploring the Maze of Multilingual Modeling—0
MCUBench: A Benchmark of Tiny Object Detectors on MCUs—0
Measures of Information Reflect Memorization Patterns—0
Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Median Selection Subset Aggregation for Parallel Inference—0
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications—0
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