Results 1 to 10 of about 283,145 (183)
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
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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
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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
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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. During the past decades, various MIL algorithms have been proposed, among which the large margin based methods is
Teng Zhang, Hai Jin
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IET Renewable Power Generation, 2023 The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy.
Tong Liu +5 more
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Improving Adversarial Robustness of CNNs via Maximum Margin
Applied Sciences, 2022 In recent years, adversarial examples have aroused widespread research interest and raised concerns about the safety of CNNs. We study adversarial machine learning inspired by a support vector machine (SVM), where the decision boundary with maximum ...
Jiaping Wu, Zhaoqiang Xia, Xiaoyi Feng
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Dictionary Learning Guided by Minimum Class Variance Support Vector [PDF]
Jisuanji gongcheng, 2020 Existing Support Vector Guided Dictionary Learning(SVGDL) algorithm based on the principle of large-margin classification.When establishing decision-making hyperplanes,the algorithms consider only the boundary conditions of each class of encoding vectors,
WANG Xiaoming, XU Tao, RAN Biao
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Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally [PDF]
IEEE Transactions on Neural Networks, 2008 In this paper, we propose a novel large margin classifier, called the maxi-min margin machine M(4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector
K, Huang, H, Yang, I, King, M R, Lyu
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Jurnal Geografi, 2021 Penyediaan data distribusi mangrove serta perubahannya membutuhkan waktu pemrosesan yang lama jika dilakukan dengan interpretasi citra secara konvensional, apalagi jika dilakukan pada area yang luas seperti Kabupaten Kubu Raya.
Trida Ridho Fariz +3 more
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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
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