Optimal Representative Distribution Margin Machine for Multi-Instance Learning [PDF]
IEEE Access, 2020 Multi-instance learning (MIL) plays an important role in many real applications, such as image recognition and text classification. The instance-based approach selects instances in each bag to train and has drawn significant attention recently.
Tianxiang Luan +3 more
doaj +3 more sources
Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach [PDF]
Energies, 2020 This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm.
Walter M. Villa-Acevedo +2 more
doaj +3 more sources
Understanding Generalization in Quantum Machine Learning with Margins [PDF]
International Conference on Machine LearningUnderstanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across ...
Tak Hur, Daniel K. Park
semanticscholar +4 more sources
Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy [PDF]
Scientific Reports, 2020 Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity.
Pierre Leclerc +9 more
semanticscholar +5 more sources
Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning [PDF]
Cancers, 2022 Simple Summary The complete resection of the malignant tumor during surgery is crucial for the patient’s survival. To date, surgeons have been intraoperatively supported by information from a pathologist, who performs a frozen section analysis of ...
David Pertzborn +7 more
semanticscholar +4 more sources
Biomedical Optics Express, 2021 We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT).
Dan Zhu +8 more
openalex +2 more sources
Prediction of Rainfall in Australia Using Machine Learning
Information, 2022 Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic
Antonio Sarasa-Cabezuelo
doaj +2 more sources
PLoS ONE, 2021 ObjectiveTo construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning.MethodsPediatric patients aged
Jae-Geum Shim +5 more
doaj +2 more sources
Development of a machine learning-based model for predicting positive margins in high-grade squamous intraepithelial lesion (HSIL) treatment by Cold Knife Conization(CKC): a single-center retrospective study [PDF]
BMC Women's HealthObjectives This study aims to analyze factors associated with positive surgical margins following cold knife conization (CKC) in patients with cervical high-grade squamous intraepithelial lesion (HSIL) and to develop a machine-learning-based risk ...
Lin Zhang +6 more
doaj +2 more sources
Evaluating Machine Learning and Statistical Prediction Techniques in Margin Sampling Active Learning for Rapid Landslide Mapping [PDF]
GeomaticsRapid and accurate landslide detection is important for minimizing loss of life and property. Supervised machine learning has shown promise for automating landslide mapping, but it often requires thousands of labeled instances, which is impractical for ...
Jing Miao +4 more
doaj +2 more sources