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Estimation of Load Margin by Machine Learning based on Synchrophasor data
2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2019In the modern power systems, precise estimation of load margin limits becomes an important guiding the system operator to determine the operation ranges before reaching the instability. This paper proposes a model of machine learning based method to improve the load margin estimator performance.
Ansaya Treeworawet +2 more
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Multiview Large Margin Distribution Machine
IEEE Transactions on Neural Networks and Learning SystemsMargin distribution has been proven to play a crucial role in improving generalization ability. In recent studies, many methods are designed using large margin distribution machine (LDM), which combines margin distribution with support vector machine ...
Kun Hu +4 more
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Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization
Proceedings of the AAAI Conference on Artificial Intelligence, 2018Neural machine translation (NMT) heavily relies on parallel bilingual data for training. Since large-scale, high-quality parallel corpora are usually costly to collect, it is appealing to exploit monolingual corpora to improve NMT.
Yijun Wang +6 more
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Supervised and semi-supervised twin parametric-margin regularized extreme learning machine
Pattern Analysis and Applications, 2020Twin extreme learning machine (TELM) has attracted considerable attention and achieved great success in the machine learning field. However, its performance will be severely affected when outliers exist in the dataset since TELM does not consider heteroscedasticity in practical applications.
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Sparse Learning for Linear Twin Parameter-margin Support Vector Machine
Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine LearningTwin Parameter-margin support vector machine (TPMSVM) is a recent very powerful binary classifier. To improve its sparsity, a linear sparse TPMSVM (Lin-STPMSVM) is proposed in this paper. In the primal problem, the vectors defining the hyperplane are replaced with their expression in terms of the dual variables as derived from Karush Khun Tucker (KKT ...
Shuanghong Qu +2 more
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Monitoring of the power system load margin based on a machine learning technique
Electrical Engineering, 2021M. E. Bento
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Fast large-margin learning for statistical machine translation
2013Statistical Machine Translation (SMT) can be viewed as a generate-and-select process, where the selection of the best translation is based on multiple numerical features assessing the quality of a translation hypothesis. Training a SMT system consists in finding the right balance between these features, so as to produce the best possible output, and is
Wisniewski, Guillaume, Yvon, François
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Large-Margin Extreme Learning Machines With Hybrid Features for Wafer Map Defect Recognition
IEEE Transactions on Instrumentation and MeasurementThe critical information regarding the semiconductor manufacturing can be provided based on the wafer map defect patterns. Automatic wafer map defect identification with machine learning methods has recently received increasing attention.
Zhengkun Yi +6 more
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Robust classifier learning with fuzzy class labels for large-margin support vector machines
Neurocomputing, 2013Using class label fuzzification, this study develops the idea of refreshing the attitude of the difficult training examples and gaining a more robust classifier for large-margin support vector machines (SVMs). Fuzzification relaxes the specific hard-limited Lagrangian constraints of the difficult examples, extends the infeasible space of the canonical ...
Chan-Yun Yang +2 more
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