Results 141 to 150 of about 283,145 (183)

Profit margin prediction in sustainable road freight transportation using machine learning

Journal of Cleaner Production, 2021
Abstract With the increasing transportation activities, road freight transportation has caused significant impacts on sustainability. The necessity of establishing sustainable road freight transportation plans have emerged for businesses. Therefore, it is important to develop decision support models that can be used by managers for sustainable road ...
Budak, Ayşenur, Sarvari, Peiman Alipour
openaire   +1 more source

Net Profit Margin Forecasting with Machine Learning Methods in Hospital Finance Management

Journal of Health Systems and Policies, 2023
Hospital information management systems (HIMS) were managed using paper-based systems with individual efforts in the pre-computer period. Today, in parallel with technological developments, it is carried out digitally in an electronic environment.
Oğuz CECE, Mehmet GENÇTÜRK
openaire   +2 more sources

Large margin strategies in machine learning

2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2002
Controlling the capacity of a learning system in a way that does not depend on the dimensionality of the hypothesis space provides the key for effectively using large neural networks and decision trees, ensemble methods and kernel-induced feature spaces.
openaire   +1 more source

Voltage stability margin prediction by ensemble based extreme learning machine

2013 IEEE Power & Energy Society General Meeting, 2013
Voltage stability margin (VSM) evaluation is one of the essential tasks of power system voltage stability analysis. Conventional methods for VSM calculation is based on continuation-power flow technique. Recently, there is growing interest to apply artificial neural network (ANN) techniques to rapidly predict the VSM.
null Rui Zhang, null Yan Xu, null Zhao Yang Dong, null Pei Zhang, null Kit Po Wong   +4 more
openaire   +1 more source

Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning

IEEE Transactions on Neural Networks and Learning Systems, 2019
Positive and unlabeled learning (PU learning) aims to train a binary classifier based on only PU data. Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem. However, none of them fully take the data distribution information into consideration when designing the model, which hinders them from ...
Chen Gong, Tongliang Liu, Jian Yang, Dacheng Tao   +3 more
openaire   +2 more sources

Estimation of Load Margin by Machine Learning based on Synchrophasor data

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2019
In 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, Achara Pichetjamroen, Sanchai Dechanupaprittha   +2 more
openaire   +1 more source

Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Neural 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, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, Tie-Yan Liu   +6 more
openaire   +1 more source

Supervised and semi-supervised twin parametric-margin regularized extreme learning machine

Pattern Analysis and Applications, 2020
Twin 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.
openaire   +1 more source

Sparse Learning for Linear Twin Parameter-margin Support Vector Machine

Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
Twin 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, Renato De Leone, Min Huang   +2 more
openaire   +1 more source

Fast large-margin learning for statistical machine translation

2013
Statistical 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
openaire   +1 more source