Results 11 to 20 of about 1,295,192 (344)
Optimal Margin Distribution Machine for Multi-Instance Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Multi-instance learning (MIL) is a celebrated learning framework where each example is represented as a bag of instances. An example is negative if it has no positive instances, and vice versa if at least one positive instance is contained.
Teng Zhang, Hai Jin
semanticscholar +3 more sources
Voltage Stability Margin Estimation Using Machine Learning Tools
Revista Técnica Energía, 2023 Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the
Gabriel Guañuna +5 more
doaj +2 more sources
IEEE Transactions on Biomedical Engineering, 2020 Objective: To demonstrate the diagnostic ability of label-free, point-scanning, fiber-based Fluorescence Lifetime Imaging (FLIm) as a means of intraoperative guidance during oral and oropharyngeal cancer removal surgery.
Mark Marsden +9 more
openalex +3 more sources
Voting Margin: A Scheme for Error-Tolerant k Nearest Neighbors Classifiers for Machine Learning
IEEE Transactions on Emerging Topics in Computing, 2019 Machine learning (ML) techniques such as classifiers are used in many applications, some of which are related to safety or critical systems. In this case, correct processing is a strict requirement and thus ML algorithms (such as for classification) must
Shanshan Liu +3 more
openalex +3 more sources
Prediction of financial deficits of postoperative patients in the intensive care unit using machine learning [PDF]
JA Clinical ReportsBackground Operational loss, defined as unanticipated financial deficits in intensive care unit (ICU) management, is challenging to predict yet critical for hospital sustainability. This study aimed to evaluate whether machine-learning models can predict
Saori Ikumi +6 more
doaj +2 more sources
Advancing NFL win prediction: from Pythagorean formulas to machine learning algorithms [PDF]
Frontiers in Sports and Active LivingThis study evaluates the predictive performance of traditional and machine learning-based models in forecasting NFL team winning percentages over a 21-season dataset (2003–2023).
Caroline Weirich +3 more
doaj +2 more sources
Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization [PDF]
Journal of Biomedical Optics, 2021 . Significance: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes.
François Daoust +11 more
semanticscholar +3 more sources
Multiplierless and Sparse Machine Learning based on Margin Propagation Networks. [PDF]
, 2019 The new generation of machine learning processors have evolved from multi-core and parallel architectures that were designed to efficiently implement matrix-vector-multiplications (MVMs). This is because at the fundamental level, neural network and machine learning operations extensively use MVM operations and hardware compilers exploit the inherent ...
P. M. Nazreen +2 more
+6 more sources
Transactions of the Japanese Society for Artificial Intelligence, 2010 Katsuhiko Hayashi +4 more
openalex +3 more sources
Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy [PDF]
PLOS Computational Biology, 2020 AbstractComplete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak
Doruk Cakmakci +7 more
+8 more sources